Status and Prospects of Feature-Based Detection Methods for Floating Targets on Sea Surface

XU Shuwen BAI Xiaohui GUO Zixun SHUI Penglang

XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on sea surface[J]. Journal of Radars, 2020, 9(4): 684–720. DOI:  10.12000/JR20084
Citation: XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on sea surface[J]. Journal of Radars, 2020, 9(4): 684–720. DOI:  10.12000/JR20084
doi: 10.12000/JR20084
详细信息
  • 中图分类号: TN95

Status and Prospects of Feature-Based Detection Methods for Floating Targets on Sea Surface

(English)

Funds: The National Natural Science Foundation of China (61871303), The Foundation of National Key Laboratory of Electromagnetic Environment (6142403180204), The Foreign Scholars in University Research and Teaching Programs (the 111 Project) (B18039)
More Information
    Author Bio:

    XU Shuwen was born in Huangshan city in Anhui, China. He received his B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. Subsequently, he worked at the National Laboratory of Radar Signal Processing, Xidian University. He worked as a visiting professor in McMaster University, Canada in 2018. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. His research interests are in the fields of radar target detection, statistical learning, and SAR image processing. E-mail: swxu@mail.xidian.edu.cn

    BAI Xiaohui was born in Baoji, Shaanxi province in 1998. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: xhbai@stu.xidian.edu.cn

    GUO Zixun was born in Xi’an, Shaanxi province in 1994. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: zxguo_724@stu.xidian.edu.cn

    SHUI Penglang was born in Xi’an, Shaanxi province in 1967. He received his Ph.D. degree in electronic engineering from Xidian University, Xi’an, China, in 1999. He is now a professor, PhD supervisor at the Radar Signal Processing National Key Lab of Electronic Engineering from Xidian University. His main research fields are sea clutter modeling, radar target detection, and image processing. E-mail: plshui@xidian.edu.cn

    Corresponding author: XU Shuwen E-mail: swxu@mail.xidian.edu.cn
  • Figure  1.  Some common small targets on sea

    Figure  2.  Power map of measured data and amplitude fitting results

    Figure  3.  Flowchart of adaptive detection methods

    Figure  4.  SCRs for various ships at sea state 4 (VV polarization), in the case of headwind

    Figure  5.  Radar working modes that realize the “double-high” system

    Figure  6.  Flowchart of the feature-based detection methods

    Figure  7.  A plan overview of the deployment site[11]

    Figure  8.  Location of the deployment site in 2006 (OTB)[11]

    Figure  9.  Experimental cooperative boats[11]

    Figure  10.  X-band solid-state power amplifier surveillance/navigation radar[51]

    Figure  11.  Three modes of combined pulse transmission[51]

    Figure  12.  Analysis of fractal characteristics in 14 range cells[30]

    Figure  13.  Trends of H(q) in 14 range cells[30]

    Figure  14.  Hurst frequency distribution of clutter cells and target cells under HH polarization[30]

    Figure  15.  Flowchart of fractal-based detector[73]

    Figure  16.  Fractal curves[73]

    Figure  17.  Distribution of training samples and test samples on the two-dimensional feature plane[73]

    Figure  18.  Classic CFAR algorithm detection curves[73]

    Figure  19.  Flowchart of detector based on neural network prediction[33]

    Figure  20.  ROC curves of neural network-based detector and traditional Doppler CFAR detector[33]

    Figure  21.  Flowchart of detection methods based on prediction

    Figure  22.  Micro-motion signal features after sea clutter suppression (N = 256)[41]

    Figure  23.  Comparison of micro-motion target detection results based on STFT and GSTFRFT after sea clutter suppression (N = 512)[41]

    Figure  24.  ST-SFT-based micro-motion target detection results (starting time = 20 s)[42]

    Figure  25.  ST-SFRFT-based micro-motion targets detection results (starting time = 20 s)[42]

    Figure  26.  Processing flow diagram of CNN-based method[43]

    Figure  27.  Flowchart of a feature-based detector using the average speckle consistency factor[74]

    Figure  28.  Average consistency factors of pure clutter and clutter with target under four polarization channels[74]

    Figure  29.  The detection probabilities of the four detectors under four polarization channels, L = 1024[74]

    Figure  30.  Distributions of features of clutter-only vectors and vectors with target in 3D feature space

    Figure  31.  Convex hull with the original training data and convex hull with given false alarm rate

    Figure  32.  Flowchart of the decision tree-based detector[78]

    Figure  33.  Detection probabilities of polarization feature-based detector and tri-detector at HH, VV, HV, and VH polarizations for 10 datasets[76]

    Table  1.   Description of IPIX radar data collected in 1993[28,50]

    Data nameWave heights
    (m)
    Wind speed
    (km/h)
    PrimarySecondary
    #172.2998, 10, 11
    #261.1976, 8
    #300.91976, 8
    #310.91976, 8, 9
    #401.0975, 6, 8
    #540.72087, 9, 10
    #2801.61087, 10
    #3100.93376, 8, 9
    #3110.93376, 8, 9
    #3200.92876, 8, 9
    下载: 导出CSV

    Table  2.   Description of IPIX radar data collected in 1998[28,50]

    Data nameRange(m)PrimarySecondaryRadar direction
    #2022253201~40112423, 25
    #2025253201~401176, 8
    下载: 导出CSV

    Table  3.   Main characteristics of OTB MS3[11]

    ParameterValue
    Latitude34°36'55.32"S
    Longitude20°17'20.11"E
    Ground height53 m
    Antenna height56 m
    Distance to sea1.2 km
    Azimuth coverage208°~80° N (SSW-ENE)
    Range(CNR > 15 dB) 1.25~4.50 km
    Grazing angle(<15 km) 3.00°~0.16°
    Grazing angle(CNR > 15 dB) 3.0°~0.7°
    下载: 导出CSV

    Table  4.   Fynmeet system and performance specifications[11]

    System compositionSystem parametersParameter values
    TransmitterFrequency range6.5~17.5 GHz
    Peak power2 kW
    PRF range0~30 kHz
    WaveformsFixed frequency waveform, step frequency waveform, frequency agility waveform
    Agile bandwidth500 MHz pulse to pulse
    AntennaTypeDual-offset reflector
    Gain≥30 dB
    Beamwidth≤2° (3 dB beamwidth)
    Slidelobes≤–25 dB
    ReceiverDynamic range60 dB (instantaneous)/120 dB (total)
    Sensitivity0.1 m2 @ 10 km
    Instrumented range200 m~15 km
    Range gates1~64; ΔR = 100 ns, 300 ns or 400 ns
    Sampler typeIntermediate frequency sampler
    Encoding typeQuadrature
    Image rejection≤–41 dBc
    下载: 导出CSV

    Table  5.   X-band radar parameters[51]

    Radar parametersParameters setting
    Working bandX
    Frequency range9.3~9.5 GHz
    Measuring range0.0625~96 nm
    Scanning bandwidth25 MHz
    Range resolution6 m
    Pulse repetition frequency1.6 K, 3 K, 5 K和10 K
    Transmit peak power50 W
    Rotating speed of antenna2 rpm, 12 rpm, 24 rpm, 48 rpm
    Length of antenna1.8 m
    Antenna operation modeGaze, circular scanning
    Antenna polarizationHH
    Antenna horizontal beam width1.2°
    Antenna vertical beam width22°
    Information about the dataset and links to its download are available on the website of the Journal of Radar.
    下载: 导出CSV

    Table  6.   Features introduction of feature-based detection method

    Existing features
    Fractal featuresSingle fractal features[29,31,67], multifractal features[30,68], fractal features in FRFT[69-73]
    Chaotic characteristics of sea clutterCorrelation dimension, Lyapunov exponent, and Kolmogorov entropy[33]
    Features in the time domainRelative average amplitude[28], temporal information entropy[77], temporal Hurst exponent[77], the speckle consistency factor[74]
    Features in the frequency domainRelative Doppler peak height[28], relative vector-entropy[28], frequency peak-to-average ratio[77], Hurst exponent in frequency domain[78]
    Features in the time and frequency domainsMicro-Doppler features[41,42], the ridge integration of NTFD[75], the number of connected regions and the maximum size of connected regions in a binary image[75]
    Polarization featuresRelative surface scattering power[76], relative dihedral scattering power[76], and the relative volume scattering power[76]
    下载: 导出CSV

    Table  7.   Detection performances of different methods for the detection of micro-motion maritime targets[42]

    ParameterMTDFRFTWVDSPWVDST-SFTST-SFRFT
    Pd(%) (SCR = –5 dB)39.2657.2635.6855.2449.2171.35
    Pd(%) (SCR = 0 dB)52.8476.8462.2772.5863.2885.69
    下载: 导出CSV

    Table  8.   Detection results of different models(%)[43]

    ModelLeNetAlexNetGoogLeNet
    False alarm ratio1.240.040.24
    Detection probability92.2884.4490.94
    下载: 导出CSV

    Table  9.   Detection performance comparison between SVM-based detector and other detectors[77]

    DetectorsDetection results
    (HH polarization, PF = 0.001)
    SCR = –2 dBSCR=17 dB
    SVM-based detector7699
    Tri-feature-based detector5799
    Fractal-based detector1879
    下载: 导出CSV

    Table  10.   Detection performance comparisons between the decision tree-based detector and the other detectors[78]

    DetectorDetection results
    0 dB5 dB10 dB15 dB
    Decision tree-based detector0.760.840.980.99
    Tri-feature-based detector0.580.650.820.95
    Fractal-based detector0.210.320.480.68
    下载: 导出CSV
  • [1] FARINA A, GINI F, GRECO M V, et al. High resolution sea clutter data: Statistical analysis of recorded live data[J]. IEE Proceedings-Radar, Sonar and Navigation, 1997, 144(3): 121–130. doi:  10.1049/ip-rsn:19971107
    [2] CONTE E, DE MAIO A, and GALDI C. Statistical analysis of real clutter at different range resolutions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3): 903–918. doi:  10.1109/TAES.2004.1337463
    [3] WARD K, TOUGH R, and WATTS S. Sea Clutter: Scattering, the K Distribution and Radar Performance[M]. 2nd ed. London: The Institution of Engineering and Technology, 2013. doi: 10.1049/PBRA025E.
    [4] CONTE E and DE MAIO A. Mitigation techniques for Non-Gaussian sea clutter[J]. IEEE Journal of Oceanic Engineering, 2004, 29(2): 284–302. doi:  10.1109/JOE.2004.826901
    [5] WALKER D. Doppler modelling of radar sea clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 2001, 148(2): 73–80. doi:  10.1049/ip-rsn:20010182
    [6] RAYNAL A M and DOERRY A W. Doppler characteristics of sea clutter[R]. SAND2010-3828, 2010.
    [7] TOPORKOV J V and SLETTEN M A. Statistical properties of low-grazing range-resolved sea surface backscatter generated through two-dimensional direct numerical simulations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5): 1181–1197. doi:  10.1109/TGRS.2007.894442
    [8] LIU Yong, FRASIER S J, and MCINTOSH R E. Measurement and classification of low-grazing-angle radar sea spikes[J]. IEEE Transactions on Antennas and Propagation, 1998, 46(1): 27–40. doi:  10.1109/8.655448
    [9] GRECO M, STINCO P, and GINI F. Identification and analysis of sea radar clutter spikes[J]. IET Radar, Sonar & Navigation, 2010, 4(2): 239–250. doi:  10.1049/iet-rsn.2009.0088
    [10] MELIEF H W, GREIDANUS H, VAN GENDEREN P, et al. Analysis of sea spikes in radar sea clutter data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4): 985–993. doi:  10.1109/TGRS.2005.862497
    [11] The Defence, Peace, Safety, and Security Unit of the Council for Scientific and Industrial Research. The Fynmeet radar database[EB/OL]. http://www.csir.co.ca/small_boat_detection.
    [12] CHEN Xiaolong, GUAN Jian, HUANG Yong, et al. Radar low-observable target detection[J]. Science & Technology Review, 2017, 35(11): 30–38. doi:  10.3981/j.issn.1000-7857.2017.11.004
    [13] CHEN Xiaolong, GUAN Jian, HUANG Yong, et al. Radar refined processing and its applications for low-observable moving target[J]. Science & Technology Review, 2017, 35(20): 19–27. doi:  10.3981/j.issn.1000-7857.2017.20.002
    [14] WATTS S. Cell-averaging CFAR gain in spatially correlated K-distributed clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 1996, 143(5): 321–327. doi:  10.1049/ip-rsn:19960745
    [15] HE You, GUAN Jian, and MENG Xiangwei. Radar Target Detection and CFAR Processing[M]. 2nd ed. Beijing: Tsinghua University Press, 2011.
    [16] ZHOU Wei, XIE Junhao, LI Gaopeng, et al. Robust CFAR detector with weighted amplitude iteration in nonhomogeneous sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1520–1535. doi:  10.1109/TAES.2017.2671798
    [17] KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(2): 115–127. doi:  10.1109/TAES.1986.310745
    [18] ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208–216. doi:  10.1109/7.135446
    [19] GINI F and GRECO M. Texture modelling, estimation and validation using measured sea clutter data[J]. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(3): 115–124. doi:  10.1049/ip-rsn:20020272
    [20] CONTE E, LOPS M, and RICCI G. Asymptotically optimum radar detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(2): 617–625. doi:  10.1109/7.381910
    [21] RICHMOND C D. Analysis of an adaptive detection algorithm for non-homogeneous environments[C]. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, USA, 1998: 2005–2008.
    [22] JAY E, OVARLEZ J P, DECLERCQ D, et al. BORD: Bayesian optimum radar detector[J]. Signal Processing, 2003, 83(6): 1151–1162. doi:  10.1016/S0165-1684(03)00034-3
    [23] DONG Yunhan. Optimal coherent radar detection in a K-distributed clutter environment[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 283–292. doi:  10.1049/iet-rsn.2011.0273
    [24] SANGSTON K J, GINI F, and GRECO M S. Coherent radar target detection in heavy-tailed compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64–77. doi:  10.1109/TAES.2012.6129621
    [25] SHANG X and SONG H. Radar detection based on Compound-Gaussian model with inverse gamma texture[J]. IET Radar, Sonar & Navigation, 2011, 5(3): 315–321. doi:  10.1049/iet-rsn.2010.0125
    [26] XU Shuwen, XUE Jian, and SHUI Penglang. Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture[J]. Digital Signal Processing, 2016, 56: 132–139. doi:  10.1016/j.dsp.2016.06.009
    [27] SHUI Penglang, LIU Ming, and XU Shuwen. Shape-parameter-dependent coherent radar target detection in K-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 451–465. doi:  10.1109/TAES.2015.140109
    [28] SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi:  10.1109/TAES.2014.120657
    [29] LO T, LEUNG H, LITVA J, et al. Fractal characterisation of sea-scattered signals and detection of sea-surface targets[J]. IEE Proceedings F-Radar and Signal Processing, 1993, 140(4): 243–250. doi:  10.1049/ip-f-2.1993.0034
    [30] HU Jing, TUNG W W, and GAO Jianbo. Detection of low observable targets within sea clutter by structure function based multifractal analysis[J]. IEEE Transactions on Antennas and Propagation, 2006, 54(1): 136–143. doi:  10.1109/TAP.2005.861541
    [31] GUAN Jian, LIU N B, HUANG Y, et al. Fractal characteristic in frequency domain for target detection within sea clutter[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 293–306. doi:  10.1049/iet-rsn.2011.0250
    [32] CHEN Xiaolong, GUAN Jian, HE You, et al. Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain[J]. IET Radar, Sonar & Navigation, 2013, 7(6): 635–651. doi:  10.1049/iet-rsn.2012.0116
    [33] HAYKIN S and LI Xiaobo. Detection of signals in chaos[J]. Proceedings of the IEEE, 1995, 83(1): 95–122. doi:  10.1109/5.362751
    [34] HE Nan and HAYKIN S. Chaotic modelling of sea clutter[J]. Electronics Letters, 1992, 28(22): 2076–2077. doi:  10.1049/el:19921331
    [35] HAYKIN S and THOMSON D J. Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem[J]. Proceedings of the IEEE, 1998, 86(11): 2325–2344. doi:  10.1109/5.726792
    [36] DJUROVIĆ I and STANKOVIĆ L. An algorithm for the Wigner distribution based instantaneous frequency estimation in a high noise environment[J]. Signal Processing, 2004, 84(3): 631–643. doi:  10.1016/j.sigpro.2003.12.006
    [37] HAYKIN S and BHATTACHARYA T K. Modular learning strategy for signal detection in a nonstationary environment[J]. IEEE Transactions on Signal Processing, 1997, 45(6): 1619–1637. doi:  10.1109/78.600003
    [38] PANAGOPOULOS S and SORAGHAN J J. Small-target detection in sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(7): 1355–1361. doi:  10.1109/TGRS.2004.827259
    [39] ZHANG Xiaowei, YANG Dongdong, GUO Jianxin, et al.. Weak moving target detection based on short-time fourier transform in sea clutter[C]. The IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2019: 415–419. doi: 10.1109/SIPROCESS.2019.8868771.
    [40] CHEN Xiaolong, GUAN Jian, HUANG Yong, et al. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2225–2240. doi:  10.1109/TGRS.2014.2358456
    [41] CHEN Xiaolong, LIU Ningbo, WANG Guoqing, et al. Gaussian short-time fractional Fourier transform based detection algorithm of target with Micro-Motion at sea[J]. Acta Electronica Sinica, 2014, 42(5): 971–977. doi:  10.3969/j.issn.0372-2112.2014.05.021
    [42] CHEN Xiaolong, GUAN Jian, YU Xiaohan, et al. Radar Micro-Doppler signature extraction and detection via short-time sparse time-frequency distribution[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1017–1023. doi:  10.11999/JEIT161040
    [43] SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. Detection and classification of maritime target with micro-motion based on CNNs[J]. Journal of Radars, 2018, 7(5): 565–574. doi:  10.12000/JR18077
    [44] MOU Xiaoqian, CHEN Xiaolong, GUAN Jian, et al. Marine target detection based on improved faster R-CNN for navigation radar PPI images[C]. 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, China, 2019: 1–5. doi: 10.1109/ICCAIS46528.2019.9074588.
    [45] LEUNG H, DUBASH N, and XIE Nan. Detection of small objects in clutter using a GA-RBF neural network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1): 98–118. doi:  10.1109/7.993232
    [46] METCALF J, BLUNT S D, and HIMED B. A machine learning approach to cognitive radar detection[C]. 2015 IEEE Radar Conference, Arlington, USA, 2015: 1405–1411.
    [47] CALLAGHAN D, BURGER J, and MISHRA A K. A machine learning approach to radar sea clutter suppression[C]. 2017 IEEE Radar Conference, Seattle, USA, 2017: 1222–1227.
    [48] MACHADO J R F and VIDAL J D L C B. Improved shape parameter estimation in K clutter with neural networks and deep learning[J]. International Journal of Interactive Multimedia and Artificial Intelligence, 2016, 3(7): 96–103. doi:  10.9781/ijimai.2016.3714
    [49] DEL-REY-MAESTRE N, MATA-MOYA D, JARABO-AMORES M P, et al. Artificial intelligence techniques for small boats detection in radar clutter. Real data validation[J]. Engineering Applications of Artificial Intelligence, 2018, 67: 296–308. doi:  10.1016/j.engappai.2017.10.005
    [50] The IPIX radar database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 2001.
    [51] LIU Ningbo, DONG Yunlong, WANG Guoqing, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8(5): 656–667. doi:  10.12000/JR19089
    [52] ANTIPOV I. Analysis of sea clutter data[R]. Technical Report, DSTO-TR-0647, 1998.
    [53] DONG Yunhan and MERRETT D. Statistical measures of S-band sea clutter and targets[R]. Technical Report, DSTO-TR-2221, 2008.
    [54] DONG Yunhan and MERRETT D. Analysis of L-band multi-channel sea clutter[R]. Technical Report, DSTO-TR-2455, 2010.
    [55] DALEY J C, RANSONE J T, BURKETT J A, et al. Sea clutter measurements on four frequencies[R]. Naval Research Laboratory Report 6806, 1968. doi: 10.1109/7.993229.
    [56] CARRETERO-MOYA J, GISMERO-MENOYO J, BLANCO-DEL-CAMPO Á, et al. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2024–2037. doi:  10.1109/TGRS.2009.2033193
    [57] SIEGEL A, OCHADLICK A, DAVIS J, et al. Spatial and temporal correlation of LOGAN-1 high-resolution radar sea clutter data[C]. Proceedings of IGARSS’94-1994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 1994: 818–821. doi: 10.1109/IGARSS.1994.399273.
    [58] RINO C L, ECKERT E, SIEGEL A, et al. X-band low-grazing-angle ocean backscatter obtained during LOGAN 1993[J]. IEEE Journal of Oceanic Engineering, 1997, 22(1): 18–26. doi:  10.1109/48.557536
    [59] HAIR T, LEE T, and BAKER C J. Statistical properties of multifrequency high-range-resolution sea reflections[J]. IEE Proceedings F-Radar and Signal Processing, 1991, 138(2): 75–79. doi:  10.1049/ip-f-2.1991.0012
    [60] ISHII S, SAYAMA S, and MIZUTANI K. Effect of changes in sea-surface state on statistical characteristics of sea clutter with X-band radar[J]. Wireless Engineering and Technology, 2011, 2(3): 175–183. doi:  10.4236/wet.2011.23025
    [61] FABBRO V, BIEGEL G, FÖRSTER J, et al. Measurements of sea clutter at low grazing angle in Mediterranean coastal environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11): 6379–6389. doi:  10.1109/TGRS.2017.2727057
    [62] AL-ASHWAL W A, BAKER C J, BALLERI A, et al. Statistical analysis of simultaneous monostatic and bistatic sea clutter at low grazing angles[J]. Electronics Letters, 2011, 47(10): 621–622. doi:  10.1049/el.2011.0557
    [63] AL-ASHWAL W A, WOODBRIDGE K, and GRIFFITHS H D. Analysis of bistatic sea clutter-Part I: Average reflectivity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1283–1292. doi:  10.1109/TAES.2014.120426
    [64] AL-ASHWAL W A, WOODBRIDGE K, and GRIFFITHS H D. Analysis of bistatic sea clutter-Part II: Amplitude statistics[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1293–1303. doi:  10.1109/TAES.2014.120434
    [65] RITCHIE M, STOVE A, WOODBRIDGE K, et al. NetRAD: Monostatic and bistatic sea clutter texture and Doppler spectra characterization at S-Band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9): 5533–5543. doi:  10.1109/TGRS.2016.2567598
    [66] FIORANELLI F, RITCHIE M, GRIFFITHS H, et al. Analysis of polarimetric bistatic sea clutter using the NetRAD radar system[J]. IET Radar, Sonar & Navigation, 2016, 10(8): 1356–1366. doi:  10.1049/iet-rsn.2015.0416
    [67] LIU Ningbo, HUANG Yong, GUAN Jian, et al. Fractal analysis of real sea clutter in frequency domain[J]. Journal of Electronics & Information Technology, 2012, 34(4): 929–935. doi:  10.3724/SP.J.1146.2011.00856
    [68] GAO Jianbo and YAO K. Multifractal features of sea clutter[C]. The 2002 IEEE Radar Conference, Long Beach, USA, 2002: 500–505.
    [69] LIU Ningbo, WANG Guoqing, BAO Zhonghua, et al. Multifractal property of sea clutter FRFT spectrum for target detection[J]. Signal Processing, 2013, 29(1): 1–9. doi:  10.3969/j.issn.1003-0530.2013.01.001
    [70] GU Zhimin, ZHANG Xinggan, and WANG Qiong. Multifractal property and target detection of sea clutter in FRFT domain[J]. Journal of Nanjing University: Natural Science, 2017, 53(4): 731–737. doi:  10.13232/j.cnki.jnju.2017.04.016
    [71] TIAN Yufang, JI Guangrong, YIN Zhiying, et al. Weak targets detection in sea clutter based on modified fractal character differences[J]. Periodical of Ocean University of China, 2013, 43(3): 92–97.
    [72] LIU Ningbo, GUAN Jian, WANG Guoqing, et al. Target detection within sea clutter based on multi-scale Hurst exponent in FRFT domain[J]. Acta Electronica Sinica, 2013, 41(9): 1847–1853. doi:  10.3969/j.issn.0372-2112.2013.09.029
    [73] SHI Yanling, ZHANG Xueliang, and LIU Zipeng. Floating small target detection in sea clutter based on jointed features in FRFT domain[C]. The 3rd EAI International Conference on Advanced Hybrid Information Processing, Nanjing, China, 2019: 128–139. doi: 10.1007/978-3-030-36405-2_14.
    [74] SHI Yanling, XIE Xiaoyan, and LI Dongchen. Range distributed floating target detection in sea clutter via feature-based detector[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1847–1850. doi:  10.1109/LGRS.2016.2614750
    [75] SHI Sainan and SHUI Penglang. Sea-surface Floating small target detection by one-class classifier in time-frequency feature space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6395–6411. doi:  10.1109/TGRS.2018.2838260
    [76] XU Shuwen, ZHENG Jibin, PU Jia, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1505–1509. doi:  10.1109/LGRS.2018.2852560
    [77] LI Yuzhou, XIE Pengcheng, TANG Zeshen, et al. SVM-based sea-surface small target detection: A false-alarm-rate-controllable approach[J]. IEEE Geoscience And Remote Sensing Letters, 2019, 16(8): 1225–1229. doi:  10.1109/LGRS.2019.2894385
    [78] ZHOU Hongkun and JIANG Tao. Decision tree based sea-surface weak target detection with false alarm rate controllable[J]. IEEE Signal Processing Letters, 2019, 26(6): 793–797. doi:  10.1109/LSP.2019.2909584
    [79] MANDELBROT B B. The Fractal Geometry of Nature[M]. San Francisco: Freeman, 1982.
    [80] MANDELBROT B B and VAN NESS J W. Fractional Brownian motions, fractional noises and applications[J]. SIAM Review, 1968, 10(4): 422–437. doi:  10.1137/1010093
    [81] JAGGARD D L and SUN Xiaoguang. Scattering from fractally corrugated surfaces[J]. Journal of the Optical Society of America A, 1990, 7(6): 1131–1139. doi:  10.1364/JOSAA.7.001131
    [82] SAVAIDIS S, FRANGOS P, JAGGARD D L, et al. Scattering from fractally corrugated surfaces: An exact approach[J]. Optics Letters, 1995, 20(23): 2357–2359. doi:  10.1364/OL.20.002357
    [83] FRANCESCHETTI G, IODICE A, MIGLIACCIO M, et al. Scattering from natural rough surfaces modeled by fractional Brownian motion two-dimensional processes[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(9): 1405–1415. doi:  10.1109/8.793320
    [84] BERIZZI F and MESE E D. Fractal theory of sea scattering[C]. International Radar Conference, Beijing, China, 1996: 661–665.
    [85] BERIZZI F, GRECO M, and VERRAZZANI L. Fractal approach for sea clutter generation[J]. IEE Proceedings-Radar, Sonar and Navigation, 2000, 147(4): 189–198. doi:  10.1049/ip-rsn:20000465
    [86] GUAN Jian, LIU Ningbo, ZHANG Jian, et al. Multifractal correlation characteristic for radar detecting low-observable target in sea clutter[J]. Signal Processing, 2010, 90(2): 523–535. doi:  10.1016/j.sigpro.2009.07.021
    [87] LIU Ningbo, GUAN Jian, and SONG Jie. Local Multifractal characteristic of sea clutter in radar scanning mode for target detection[J]. Radar Science and Technology, 2009, 7(4): 277–283. doi:  10.3969/j.issn.1672-2337.2009.04.008
    [88] GUAN Jian, LIU Ningbo, ZHANG Jian, et al. Multifractal correlation characteristic of real sea clutter and low-observable targets detection[J]. Journal of Electronics & Information Technology, 2010, 32(1): 54–61. doi:  10.3724/SP.J.1146.2008.00980
    [89] LEUNG H and LO T. Chaotic radar signal processing over the sea[J]. IEEE Journal of Oceanic Engineering, 1993, 18(3): 287–295. doi:  10.1109/JOE.1993.236367
    [90] HENNESSEY G, LEUNG H, DROSOPOULOS A, et al. Sea-clutter modeling using a radial-basis-function neural network[J]. IEEE Journal of Oceanic Engineering, 2001, 26(3): 358–372. doi:  10.1109/48.946510
    [91] LEUNG H, HENNESSEY G, and DROSOPOULOS A. Signal detection using the radial basis function coupled map lattice[J]. IEEE Transactions on Neural Networks, 2000, 11(5): 1133–1151. doi:  10.1109/72.870045
    [92] XIE Nan and LEUNG H. Reconstruction of piecewise chaotic dynamic using a genetic algorithm multiple model approach[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2004, 51(6): 1210–1222. doi:  10.1109/TCSI.2004.826216
    [93] XIE Nan, LEUNG H, and CHAN H. A multiple-model prediction approach for sea clutter modeling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1491–1502. doi:  10.1109/TGRS.2003.811690
    [94] HAYKIN S, BAKKER R, and CURRIE B W. Uncovering nonlinear dynamics-the case study of sea clutter[J]. Proceedings of the IEEE, 2002, 90(5): 860–881. doi:  10.1109/JPROC.2002.1015011
    [95] UNSWORTH C P, COWPER M R, MCLAUGHLIN S, et al. Re-examining the nature of radar sea clutter[J]. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(3): 105–114. doi:  10.1049/ip-rsn:20020301
    [96] GAO J B, HWANG S K, CHEN H F, et al. Can sea clutter and indoor radio propagation be modeled as strange attractors?[C]. The 7th Experimental Chaos Conference, Melville, 2003: 25–29.
    [97] MCDONALD M and DAMINI A. Limitations of nonlinear chaotic dynamics in predicting sea clutter returns[J]. IEE Proceedings-Radar, Sonar and Navigation, 2004, 151(2): 105–113. doi:  10.1049/ip-rsn:20040261
    [98] WANG Jun, ZHENG Tong, LEI Peng, et al. Study on deep learning in radar[J]. Journal of Radars, 2018, 7(4): 395–411. doi:  10.12000/JR18040
    [99] WOOD J C and BARRY D T. Linear signal synthesis using the Radon-Wigner transform[J]. IEEE Transactions on Signal Processing, 1994, 42(8): 2105–2111. doi:  10.1109/78.301845
    [100] WOOD J C and BARRY D T. Radon transformation of time-frequency distributions for analysis of multicomponent signals[J]. IEEE Transactions on Signal Processing, 1994, 42(11): 3166–3177. doi:  10.1109/78.330375
    [101] WANG Minsheng, CHAN A K, and CHUI C K. Linear frequency-modulated signal detection using Radon-ambiguity transform[J]. IEEE Transactions on Signal Processing, 1998, 46(3): 571–586. doi:  10.1109/78.661326
    [102] MANN S and HAYKIN S. The chirplet transform: Physical considerations[J]. IEEE Transactions on Signal Processing, 1995, 43(11): 2745–2761. doi:  10.1109/78.482123
    [103] SHUI Penglang, BAO Zheng, and SU Hongtao. Nonparametric detection of FM signals using time-frequency ridge energy[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1749–1760. doi:  10.1109/TSP.2007.909322
    [104] WANG Shiqing and JIN Yaqiu. Ship wake detection in SAR images based on radon transformation and morphologic image processing[J]. Journal of Remote Sensing, 2001, 5(4): 289–294. doi:  10.3321/j.issn:1007-4619.2001.04.008
    [105] TANG Ziyue, ZHU Minhui, and WANG Weiyan. A CFAR detection method of ship wakes in SAR images[J]. Acta Electronica Sinica, 2002, 30(9): 1336–1339. doi:  10.3321/j.issn:0372-2112.2002.09.022
    [106] GUAN Jian, LI Bao, LIU Jianeng, et al. Two approaches of detecting weak moving target with constant acceleration in sea clutter[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1898–1902. doi:  10.3724/SP.J.1146.2008.01023
    [107] CARRETERO-MOYA J, GISMERO-MENOYO J, ASENSIO-LOPEZ A, et al. Application of the radon transform to detect small-targets in sea clutter[J]. IET Radar, Sonar & Navigation, 2009, 3(2): 155–166. doi:  10.1049/iet-rsn:20080123
    [108] LIU Jiancheng, WANG Xuesong, LIU Zhong, et al. Detection performance of linear frequency modulated signals based on Wigner-Hough transform[J]. Acta Electronica Sinica, 2007, 35(6): 1212–1217. doi:  10.3321/j.issn:0372-2112.2007.06.041
    [109] ZHANG Yu, QIAN S, and THAYAPARAN T. Detection of a manoeuvring air target in strong sea clutter via joint time-frequency representation[J]. IET Signal Processing, 2008, 2(3): 216–222. doi:  10.1049/iet-spr:20070047
    [110] SHUI Penglang, LIU Hongwei, and BAO Zheng. Range-spread target detection based on cross time-frequency distribution features of two adjacent received signals[J]. IEEE Transactions on Signal Processing, 2009, 57(10): 3733–3745. doi:  10.1109/TSP.2009.2029715
    [111] ZUO Lei, LI Ming, ZHANG Xiaowei, et al. Small-target detection in sea clutter based on improved Hough transform[J]. Journal of Electronics & Information Technology, 2012, 34(4): 923–928.
    [112] ZUO Lei, LI Ming, ZHANG Xiaowei, et al. An efficient method for detecting slow-moving weak targets in sea clutter based on time-frequency iteration decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3659–3672. doi:  10.1109/TGRS.2012.2224665
    [113] ZUO Lei, LI Ming, ZHANG Xiaowei, et al. CFAR detection of range-spread targets based on the time-frequency decomposition feature of two adjacent returned signals[J]. IEEE Transactions on Signal Processing, 2013, 61(24): 6307–6319. doi:  10.1109/TSP.2013.2282274
    [114] CHEN Xiaolong, HUANG Yong, GUAN Jian, et al.. Sea clutter suppression and moving target detection method based on clutter map cancellation in FRFT domain[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 438–441. doi: 10.1109/CIE-Radar.2011.6159571.
    [115] GUAN Jian, CHEN Xiaolong, HUANG Y, et al. Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 389–401.
    [116] CHEN Xiaolong, GUAN Jian, BAO Zhonghua, et al. Detection and extraction of target with micromotion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1002–1018. doi:  10.1109/TGRS.2013.2246574
    [117] JANGAL F, SAILLANT S, and HELIER M. Wavelet contribution to remote sensing of the sea and target detection for a high-frequency surface wave radar[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3): 552–556. doi:  10.1109/LGRS.2008.923211
    [118] CHEN Xiaolong, DONG Yunlong, LI Xiuyou, et al. Modeling of micromotion and analysis of properties of rigid marine targets[J]. Journal of Radars, 2015, 4(6): 630–638. doi:  10.12000/JR15079
    [119] POSNER F L. Spiky sea clutter at high range resolutions and very low grazing angles[J]. IEEE Transactions on Aerospace and Electronic systems, 2002, 38(1): 58–73. doi:  10.1109/7.993229
    [120] WANG Pu, LI Hongbin, DJUROVIC I, et al. Integrated cubic phase function for linear FM signal analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 963–977. doi:  10.1109/TAES.2010.5545167
    [121] BI Guoan, LI Xiumei, and SEE C M S. LFM signal detection using LPP-Hough transform[J]. Signal Processing, 2011, 91(6): 1432–1443. doi:  10.1016/j.sigpro.2010.08.001
    [122] AOI M, LEPAGE K, LIM Y, et al. An approach to time-frequency analysis with ridges of the continuous chirplet transform[J]. IEEE Transactions on Signal Processing, 2015, 63(3): 699–710. doi:  10.1109/TSP.2014.2365756
    [123] CHASSANDE-MOTTIN É and PAI A. Best chirplet chain: Near-optimal detection of gravitational wave chirps[J]. Physical Review D, 2014, 50(17): 1240–1242. doi:  10.1049/el.2014.1569
    [124] LI Dongchen, SHUI Penglang, and XU Shuwen. Floating small target detection in the sea clutter via block-whitened clutter suppression[J]. Journal of Xidian University, 2016, 43(6): 21–26. doi:  10.3969/j.issn.1001-2400.2016.06.004
    [125] COHEN L, BAI Juxian translation. Time-Frequency Analysis: Theory and Application[M]. Xi’an: Xi’an Jiaotong University Press, 1998.
    [126] RICHARD C. Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions[J]. IEEE Transactions on Signal Processing, 2002, 50(9): 2170–2176. doi:  10.1109/TSP.2002.801927
    [127] FREEMAN A and DURDEN S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963–973. doi:  10.1109/36.673687
    [128] RABIDEAU D J and PARKER P. Ubiquitous MIMO multifunction digital array radar[C]. The 37th Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, USA, 2003: 1057–1064. doi: 10.1109/ACSSC.2003.1292087.
  • [1] 陈世超, 高鹤婷, 罗丰.  基于极化联合特征的海面目标检测方法 . 雷达学报, doi: 10.12000/JR20072
    [2] 陈小龙, 陈唯实, 饶云华, 黄勇, 关键, 董云龙.  飞鸟与无人机目标雷达探测与识别技术进展与展望 . 雷达学报, doi: 10.12000/JR20068
    [3] 郭子薰, 水鹏朗, 白晓惠, 许述文, 李东宸.  海杂波中基于可控虚警K近邻的海面小目标检测 . 雷达学报, doi: 10.12000/JR20055
    [4] 左磊, 产秀秀, 禄晓飞, 李明.  基于空域联合时频分解的海面微弱目标检测方法 . 雷达学报, doi: 10.12000/JR19035
    [5] 陈世超, 罗丰, 胡冲, 聂学雅.  基于多普勒谱非广延熵的海面目标检测方法 . 雷达学报, doi: 10.12000/JR19012
    [6] 丁昊, 刘宁波, 董云龙, 陈小龙, 关键.  雷达海杂波测量试验回顾与展望 . 雷达学报, doi: 10.12000/JR19006
    [7] 何其芳, 张群, 罗迎, 李开明.  正弦调频Fourier-Bessel变换及其在微动目标特征提取中的应用 . 雷达学报, doi: 10.12000/JR17069
    [8] 苏宁远, 陈小龙, 关键, 牟效乾, 刘宁波.  基于卷积神经网络的海上微动目标检测与分类方法 . 雷达学报, doi: 10.12000/JR18077
    [9] 曾丽娜, 周德云, 李枭扬, 张堃.  基于无训练单样本有效特征的SAR目标检测 . 雷达学报, doi: 10.12000/JR16114
    [10] 陈小龙, 关键, 何友, 于晓涵.  高分辨稀疏表示及其在雷达动目标检测中的应用 . 雷达学报, doi: 10.12000/JR16110
    [11] Li Jianbing, Gao Hang, Wang Tao, Wang Xuesong.  A Survey of the Scattering Characteristics and Detection of Aircraft Wake Vortices . 雷达学报, doi: 10.12000/JR17068
    [12] 张增辉, 郁文贤.  稀疏微波SAR图像特征分析与目标检测研究 . 雷达学报, doi: 10.12000/JR15097
    [13] 丁昊, 董云龙, 刘宁波, 王国庆, 关键.  海杂波特性认知研究进展与展望 . 雷达学报, doi: 10.12000/JR16069
    [14] 王璐, 张帆, 李伟, 谢晓明, 胡伟.  基于Gabor滤波器和局部纹理特征提取的SAR目标识别算法 . 雷达学报, doi: 10.12000/JR15076
    [15] 韩萍, 王欢.  基于改进的稀疏保持投影的SAR目标特征提取与识别 . 雷达学报, doi: 10.12000/JR15068
    [16] 陈小龙, 董云龙, 李秀友, 关键.  海面刚体目标微动特征建模及特性分析 . 雷达学报, doi: 10.12000/JR15079
    [17] 孙志军, 薛磊, 许阳明, 孙志勇.  基于多层编码器的SAR目标及阴影联合特征提取算法 . 雷达学报, doi: 10.3724/SP.J.1300.2012.20085
    [18] 闫亮, 孙培林, 易磊, 韩宁, 汤俊.  基于逆高斯分布的复合高斯海杂波建模研究 . 雷达学报, doi: 10.3724/SP.J.1300.2013.13083
    [19] 陈小龙, 关键, 何友.  微多普勒理论在海面目标检测中的应用及展望 . 雷达学报, doi: 10.3724/SP.J.1300.2012.20102
    [20] 张林, 赵志坚, 关键, 何友.  基于自适应阈值选择的非参量GS 检测算法 . 雷达学报, doi: 10.3724/SP.J.1300.2012.20084
  • 加载中
图(33) / 表 (10)
计量
  • 文章访问数:  744
  • HTML全文浏览量:  474
  • PDF下载量:  241
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-06-25
  • 修回日期:  2020-08-14
  • 网络出版日期:  2020-08-28
doi: 10.12000/JR20084
    基金项目:  The National Natural Science Foundation of China (61871303), The Foundation of National Key Laboratory of Electromagnetic Environment (6142403180204), The Foreign Scholars in University Research and Teaching Programs (the 111 Project) (B18039)
    作者简介:

    XU Shuwen was born in Huangshan city in Anhui, China. He received his B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. Subsequently, he worked at the National Laboratory of Radar Signal Processing, Xidian University. He worked as a visiting professor in McMaster University, Canada in 2018. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. His research interests are in the fields of radar target detection, statistical learning, and SAR image processing. E-mail: swxu@mail.xidian.edu.cn

    BAI Xiaohui was born in Baoji, Shaanxi province in 1998. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: xhbai@stu.xidian.edu.cn

    GUO Zixun was born in Xi’an, Shaanxi province in 1994. She is now a Ph.D. student in Xidian University. Her main research fields are radar target detection, machine learning, and sea clutter signal processing. E-mail: zxguo_724@stu.xidian.edu.cn

    SHUI Penglang was born in Xi’an, Shaanxi province in 1967. He received his Ph.D. degree in electronic engineering from Xidian University, Xi’an, China, in 1999. He is now a professor, PhD supervisor at the Radar Signal Processing National Key Lab of Electronic Engineering from Xidian University. His main research fields are sea clutter modeling, radar target detection, and image processing. E-mail: plshui@xidian.edu.cn

    通讯作者: XU Shuwen E-mail: swxu@mail.xidian.edu.cn
  • 中图分类号: TN95

English Abstract

XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on sea surface[J]. Journal of Radars, 2020, 9(4): 684–720. DOI:  10.12000/JR20084
Citation: XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on sea surface[J]. Journal of Radars, 2020, 9(4): 684–720. DOI:  10.12000/JR20084
    • Various types of radar systems constitute the main equipment for the all-time monitoring of sea environments. Sea surface observation by radar technology is an important means to realize the dynamic monitoring of the sea surface and the early warning and surveillance of sea-surface targets. With the wide application and rapid development of the radar observation of the sea in the field of military science and technology and civil affairs, increasing attention has been paid to the research on electromagnetic scattering echoes from the sea surface. There are two main purposes to study the complex sea surface echoes: one is to extract the information on the ocean dynamic characteristics (such as wave height, wave direction, and wind speed) from the sea surface echo signals; the other is to detect the target (such as boats, ice floes, and aircraft) from the complex sea surface echoes, whereby the sea surface echo is a kind of interference factor, usually called sea clutter.

      Sea clutter is defined as the received backscattered echo of the sea surface when the radar electromagnetic wave illuminates the sea surface[1-4]. The physical mechanism of sea clutter is complex and depends on many factors, which include the condition of the complex sea surface and the working state of radar. Due to the influence of environmental factors and radar equipment parameters, the characteristics of sea clutter are constantly varying, and the space-time variation of sea clutter is more complicated than that of ground clutter. At present, the main difficulties in the detection of slow small targets on sea surface come from the following four aspects:

      (1) The weakness of slow small-target echo: Small sea surface-targets such as small boats, icebergs, frogmen, debris, submarine periscope, and stealthy targets, as shown in Fig. 1, have weak Radar Cross Section (RCS). Because of weak returns, these targets have a very low Signal Clutter Ratio (SCR). Although the maritime anti-intrusion radar is designed with high spatial and Doppler resolution to raise the SCR to a critical value to enable target detection, detection using the traditional detection methods is still difficult under low SCR. Meanwhile, the target moves slowly and the sea clutter has wide Doppler bandwidth, making it difficult to distinguish the target from the sea clutter in the Doppler domain; this kind of detection is usually called “super clutter detection”. Traditional adaptive detection methods are difficult to work in this case of slowly moving targets.

      Figure 1.  Some common small targets on sea

      (2) The high complexity of space-time-varying sea clutter and the difficulty of recognizing sea clutter characteristics: The range and azimuth of the radar system and high-resolution observations make the sea clutter more complex, and it becomes difficult to satisfy the traditional large-scale statistical characteristics. Influenced by numerous factors such as marine meteorology and the geographical environment, the sea surface changes nonlinearly and randomly, and the change mechanism is very complex. Moreover, the wave height increases with wind speed. When the wave is out of balance under gravity, a breaking wave is produced, and the “split structure” before the wave is completely broken makes mirror-like reflection, then a strong echo appears, which is called the sea spike[3-8]. However, the appearance of the sea spike component, which is similar to the target, complicates the clutter modeling[9,10]. Meanwhile, as our focus shifts from the nearshore region to the open sea, the maritime radar must monitor and detect a range of unfamiliar marine environments. The three “non” characteristics of sea clutter, which are non-uniform, non-stationary, and non-Gaussian characteristics, are more obvious. As shown in Fig. 2(a), the power map of sea clutter exhibits strong non-uniformity and non-stationarity. As shown in Fig. 2(b), the empirical probability density function of the clutter substantially deviates from the Rayleigh amplitude distribution and exhibits a significant non-Gaussian property. The strong non-uniformity and non-stationarity mean that the sea clutter characteristics can be considered invariable only in near-range azimuth resolution cells, and the probability of the same distribution of sea clutter decreases greatly with the increase in space range. Although the sea clutter characteristics slowly change in the slow time dimension (the same order as the time scale of the change of weather conditions and sea conditions), the large scene-scanning observation radar lacks the time resources for long-term observation, and it is difficult to obtain a large number of independent and identically distributed samples in the slow time dimension. That is, although a large number of sea clutter samples can be obtained from maritime radar, during the estimation of the sea clutter characteristics of a radar resolution cell, only a small number of local samples within a short time interval are completely independent and identically distributed. Therefore, sea clutter characteristic perception is essentially a “small sample” problem, which is called “nature small sample” problem. Meanwhile, since the sea clutter characteristics in large scenes are space-varying and time-varying, it is necessary to perceive the sea clutter characteristics varying with space and time in large scenes real-time online in order to achieve constant false alarm detection.

      Figure 2.  Power map of measured data and amplitude fitting results

      (3) The difficulty of building the target model: The motion and attitude of large ships are hardly affected by waves, while those of small targets on the sea surface are severely disturbed by wind and waves. Due to the complex motion patterns (including roll, pitch, yaw) of small targets on the sea surface, the targets may be partially or even completely blocked by waves for a period; thus, small-target returns have severe RCS fluctuations and complex amplitude and Doppler modulation phenomena. There are many kinds of targets on the sea surface, which can be divided into the following three types: (i) flying targets over the sea surface, which can be easily detected by radar because of their high speed and strong maneuverability; (ii) large targets on the sea surface, such as large ships and cruise ships, which have larger RCS and can be effectively detected; (iii) SFTs on the sea surface, such as aircraft wreckage, frogman, and periscope. The RCS of the SFTs is small, and returns are often submerged in the sea clutter, which is not easy to detect. How to effectively detect this kind of low-observable targets in the sea clutter background is a problem that researchers have been exploring[12,13]. Therefore, it is difficult to construct a simple parametric model of target similar to the adaptive detection method to describe the returns of the target of interest, and it is necessary to develop new feature-based detection methods.

      (4) The non-equilibrium of target and sea clutter: In the observation of the sea, the scene is large, but the amount of small and weak targets on the sea surface is limited. Therefore, most of the received data are sea clutter, and the amount of the target returns is much less than sea clutter data; this problem is called “class non-equilibrium problem” in machine learning and pattern recognition and is caused by the sparsity of the target relative to the sea clutter. The traditional methods in pattern recognition and machine learning may degrade the classification performance when dealing with class-imbalance problems. Therefore, the traditional machine learning method also faces challenges in the detection of SFTs in the sea clutter background.

      Because of the variety of target types and the change of radar systems, the detection of SFTs on the sea surface has been long discussed. With the revolution of radar systems and the emergence of new signal-processing techniques, many new problems and new methods have emerged in the field of the radar detection of sea targets. This paper first summarizes the common methods of radar target detection in the sea clutter background. Then, as an important part of the evaluation of the algorithm for the intelligent detection of targets on the sea surface, this paper introduces the datasets that are commonly used to verify the performance of the feature-based detection methods, and a series of feature-based detection methods are introduced in sequence. Finally, the prospect of feature-based target detection in the sea clutter background is discussed.

    • With the establishment of the strategy of building a powerful maritime nation, the sea radar equipment has rapidly developed, and the advantage of backwardness makes China complete the hardware “catch-up and surpass”, forming the situation of “hard surpass, soft lag” to the sea radar. A common problem for sea radars is target detection under critical SCR. The detection of low-resolution alert and early warning radar for traditional large ships is not difficult, and non-coherent or coherent detection methods are generally adopted. The non-coherent detection methods are mainly based on the amplitude and power of radar echoes. The main methods are the use of cell-average Constant False Alarm Rate (CFAR) detector and the greatest-of or the smallest-of CFAR detectors, which are often applied in the detection of early incoherent radar pulse systems[14-16]. In coherent radar pulse system, the adaptive detection method is based on a certain statistical model of sea clutter, and then a series of optimal or near-optimal detectors corresponding to the model is developed. The flowchart of the detection method is displayed in Fig. 3.

      Figure 3.  Flowchart of adaptive detection methods

      Sea clutter can be modeled as a complex Gaussian distribution model with an increase in radar resolution in the case of low grazing angle. Kelly[17] and Robey[18] proposed the generalized likelihood ratio test and Adaptive Matched Filter (AMF) detectors, respectively, which achieved good detection performance. However, in the case of high-resolution radar and low grazing angle, sea clutter has strong non-Gaussian property, which makes the complex Gaussian distribution model no longer suitable. Then, sea clutter can be modeled as a Compound Gaussian Model (CGM)[19], which is the product of a slowly varying texture component and a rapidly varying speckle component:

      $$ {{c}}\left( n \right) = \sqrt {{{\tau}} \left( n \right)} {{u}}\left( n \right) $$ (1)

      Conte[20] and Richmond[21] proposed the normalized matched filter and adaptive normalized matched filter detectors based on the CGM. In a coherent processing interval, the CGM can be reduced to a spherically invariant random vector, in which the texture component is a positive random constant, and the speckle component obeys the complex Gaussian distribution:

      $$ {{c}} = \sqrt {{\tau}} {{u}} $$ (2)

      Texture components obey the Gamma distribution, the inverse Gamma distribution, and the inverse Gaussian distribution. Based on this, the Optimum K-distributed Detector (OKD)[22,23], the Generalized Likelihood Ratio Test-Linear Threshold Detector (GLRT-LTD)[24,25], and the Generalized Likelihood Ratio Test with Inverse Gaussian texture (GLRT-IG)[26] have been developed. In addition, because of the engineering unrealizability of the OKD, a shape parameter-dependent adaptive detector called α-AMF[27] has been proposed, which has a simpler structure than the OKD, has a detection performance similar to that of the OKD, and is engineering realizable. The adaptive detection method is suitable for short-time coherent accumulation and is often used to detect large targets and moving targets on the sea surface. This kind of detector is mainly used in wide-area surveillance radar systems. Because of the scanning efficiency, the number of accumulated pulses is limited in the dwell time of a beam position. However, sea clutter has a wide Doppler bandwidth. When the velocity of the target is low or the RCS is small, the target return may be submerged in the strong sea clutter, and the performance of the adaptive detection method largely degrades. Therefore, it is difficult for this method to detect SFTs on the sea surface. The SCR of the target returns can be estimated by the power in target range cells. First, the average power ${\bar p_c}$ of the sea clutter is estimated from the pure clutter cells. If the target returns and sea clutter are assumed to be independent, then the average SCR can be estimated by Eq. (3):

      $$ {\rm{ASCR = 10lg}}\left( {\frac{{{1/N}\displaystyle\sum\limits_{n = 1}^N {{{\left| {{{x}}(n)} \right|}^2}} - {{\bar p}_c}}}{{{{\bar p}_c}}}} \right) $$ (3)

      where ${{x}}(n)$ is the echo sequence of the target range cell, and N is the sequence length. The actual SCR fluctuates around the average SCR.

      For the detection of small RCS targets such as stealth vessels, the traditional detection method features a critical SCR and thus requires “cognitive sea clutter suppression”. For example, the littoral combat ship has an RCS of about 100 m2, the Zumwalt-class destroyer DDG1000 has an RCS of only 60 m2, and the stealth ship has an RCS of only 30 m2. The appearance of these stealth ships also makes the target detection more difficult for low-resolution-surveillance early warning radars. The Technology Service Corporation (TSC) model is used to simulate the SCR of different ships in the case of VV polarization and headwind situation at sea level 4. As shown in Fig. 4, it is difficult to detect ships with 30 m2 and 60 m2 RCS in the range of 50~300 km through the traditional method. Therefore, it is necessary to change the radar working system and develop new target detection methods.

      Figure 4.  SCRs for various ships at sea state 4 (VV polarization), in the case of headwind

    • In addition to large ship targets, sea-surface targets also include small slow targets at close range. Due to the “spatial resolution performance bottleneck”, as mentioned above, it is difficult to detect these targets using low-resolution radar. Traditional target detection methods such as moving target indication, Moving Target Detection (MTD), and adaptive detection methods are based on accurate statistical models for clutter and target; therefore, all kinds of optimal and near-optimal test statistics play a key role in detection. With the refinement of observation means, background clutter and target returns become complex, and it is difficult to build an accurate statistical model. In this case, the “double-high” system with high spatial resolution and high Doppler resolution is the main technical approach. At present, there are three radar working modes to realize the “double-high” system. They are the wide transmitting beam and narrow receiving beam model, ubiquitous radar system, and quick scanning plus beam park model for suspected targets (Fig. 5).

      Figure 5.  Radar working modes that realize the “double-high” system

      High-resolution small-target detection radar faces the problems of extremely complex high-resolution sea clutter characteristics and small-target characteristics, and the key to breaking through the critical SCR and detection is deep cognition, elaborate perception, and the full utilization of sea clutter. In this case, one or more different features of clutter and target returns are usually extracted to realize the combination detection, and this technique is called feature-based detection. The feature-based detection method has been widely used in image processing, pattern recognition, and other fields and has produced good results. This method can be considered a preliminary version of intelligent detection and is a necessary technique in the era of intelligent detection. The detection method based on multiple features in sea clutter background is to reduce the observation space with high overlap to the feature space with low overlap by extracting different features between clutter and target returns, and detect targets in the feature space. The traditional radar target detection algorithm is based on a specific test statistic, and its performance is limited in SFT detection in the sea clutter background. Therefore, the multi-feature combination method is applied to small-target detection, and the tri-feature-based detector[28] has been proposed, which improves the performance loss of the traditional method. A feature is an index used to describe the difference between clutter and target. Feature-based detection is not limited to specific features but is based on the actual environment and radar equipment information, and the detection method can be based on different features extracted from the amplitude, Doppler spectrum, Time-Frequency (TF) image, and polarization information of radar returns. Moreover, with the increase in the number of features, the difference between target and clutter can be reflected more completely. The method based on machine learning algorithms to detect targets in high-dimensional feature space has been proposed, which greatly improves the detection performance. To solve the problem of feature-based detection in sea clutter background, scholars have started relevant research and have obtained a large number of results and proposed many kinds of feature-based detection methods, for example, detectors based on the fractal and chaotic features of the sea clutter, TF analysis, and artificial intelligence. Fig. 6 displays the flowchart of the feature-based detection methods. These methods are briefly summarized below and are described in more detail in Section 4.

      Figure 6.  Flowchart of the feature-based detection methods

      (1) Sea-surface target detection methods based on fractal and chaotic features: The fractal theory proposed by Mandelbrot in 1970s, an active branch of nonlinear science, is used to represent complex graphs and complex processes. According to the different fractal characteristics between the sea clutter and the target, a new target detection method based on the fractal theory was proposed by Lo T[29] from McMaster University in 1993. In 2006, Hu Jing[30] introduced a sea-surface target detection algorithm based on the multifractal theory. By analysis of the measured sea clutter data, it has been found that the sea clutter has multifractal characteristics in the time scale range of 0.01 s to several seconds, and the Hurst exponent of sea clutter is calculated to detect the target, which has good detection performance. He You, Guan Jian, Liu Ningbo, Chen Xiaolong[31,32] from Naval Aeronautical University have also performed much work in this field. The target detection methods based on the fractal theory are simple and efficient, but because the fractal characteristics of sea clutter time series exist only in the range of a certain time scale, which varies with the change of radar parameters, sea state, and polarization, the detector performance is affected by estimation errors in the scaling range. When the observation time is long, this kind of method can exhibit good detection performance, but if the observation time decreases, the detector performance will remarkably decline. In 1995, Simon Haykin[33,34] from McMaster University proposed that sea clutter is chaotic and predictable in the short term. According to the short-term predictability of sea clutter, a nonlinear prediction model is constructed for sea clutter time sequence, and the prediction error is used to test statistical hypotheses. Haykin later questioned the sea clutter chaotic model, and when the SCR is continuously reduced, the detection performance is not ideal, and the detection of slow small targets becomes difficult.

      (2) Sea-surface target detection methods based on TF analysis: In recent years, many methods based on TF analysis have been proposed to detect sea-surface targets[35-40]. Through TF analysis tools such as Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD), Smoothed Pseudo-Wigner-Ville Distribution (SPWVD), and FRactional Fourier Transform (FRFT), the feature corresponding to the target is extracted by parametric or non-parametric methods in a 2D plane to detect the target in the sea clutter background. More details and information can be obtained by TF analysis, which overcomes the shortcoming of the complete separation between the time domain and the frequency domain by Fourier analysis and balances both the time domain and frequency domain. However, according to the Heisenberg uncertainty principle, the longer the time window function in the STFT, the higher the frequency resolution and the worse the time resolution. The resolution in the time and frequency domains can be improved by using the bilinear Wigner-Ville transform, but due to the nonlinear transformation, cross-terms will appear when multiple signal components are present, which will reduce the detector performance for slow small targets. Although the SPWVD can suppress the cross-terms, the algorithm efficiency is sacrificed, and the joint TF resolution is also reduced. Although the TF analysis method based on FRFT has good detection performance for large ship targets without the occurrence of cross-terms, it is still not good for slow small targets on the sea surface. Due to the complex motion of the slow small targets in the sea, the target signal usually appears as a weak nonlinear Frequency Modulation (FM) signal, and consequently, the detection method faces many challenges in practical applications. In recent years, the micro-Doppler theory has been studied. Micro-Doppler is caused by the Doppler shift of the target, which reflects the change of characteristics in the Doppler domain. The micro-states of the target can well reflect the refined characteristics of the target, so the characteristics of the micro-moving target can be effectively distinguished from the sea clutter. The detection method based on micro-Doppler features is to establish the target echo signal as an appropriate model and to analyze the micro-moving features by Fourier transform or TF analysis to enhance the detection performance of slow small targets on the sea surface. The existing methods of sea-surface target detection based on the micro-Doppler theory include sea-surface micro-motion target detection method based on Gaussian Short-Time FRactional Fourier Transform (GSTFRFT)[41], micro-Doppler features extraction and detection via Short-Time sparse TF Distribution (ST-STFD)[42], and detection and classification of maritime micro-motion targets based on Convolutional Neural Network (CNN)[43].

      (3) Sea-surface target detection methods based on artificial intelligence: Sea-surface target detection based on artificial intelligence methods originated from the neural network method proposed by Haykin[33] and has made some progress. With the development of artificial intelligence and machine learning, some scholars have proposed new detection methods of sea-surface targets[44-48]. In recent years, Nerea del-Rey-Maestre and David Mata-Moya[49] applied artificial intelligence to sea-surface target detection and verified the detection method by the measured data. In their compound hypothesis testing problem, the neural network detector could be approximated to a Neyman-Pearson criterion detector. They analyzed the suboptimal method based on Constrained Generalized Likelihood Ratio (CGLR) and compared it with the conventional method based on Doppler filtering. Artificial intelligence solutions based on second-order neural networks provide the best performance and approximate CGLR in real time and at very low computational cost.

    • The detector performance needs to be verified by the measured data. In 1993 and 1998, a group led by Professor Haykin[50] of McMaster University in Canada collected large amounts of high-resolution sea clutter data by the intelligent pixel (IPIX) processing band X-band radar and shared them on its website. The IPIX radar can transmit horizontal polarization (H-polarization) and vertical polarization (V-polarization) electromagnetic waves and can receive both horizontally and vertically by two linear receivers; therefore, the measured data under four polarization channels HH, HV, VH, and VV can be obtained.

      In this paper, 10 sets of data collected in 1993 and 2 sets of data collected in 1998 are presented. There are some differences in radar location, parameters, and cooperative targets between the data of the two experiments. The radar for the 1993 data collection was deployed on a 30-meter-high cliff on the east coast of Canada, at the Dartmouth, Nova Scotia, and the investigation was directed toward the Atlantic Ocean. The test target was an anchored spherical block of Styrofoam wrapped with wire mesh of 1 m diameter. The carrier frequency of the IPIX radar was 9.3 GHz, the beam width was 0.9°, and the range resolution was 30 m. The radar worked in the dwell mode, with a Pulse Repetition Frequency (PRF) of 1000 Hz and a dwell time of about 131 s. Each set of data contained 14 range cells. As the radar illuminated the target at a low grazing angle, the undulation and oscillation of the target caused the energy diffusion, and the range oversampling was adopted in the data acquisition; therefore, the adjacent range cells around the target range cell will be affected by the energy of the target and are regarded as secondary cells. The wind speed and wave height for the 1993 measured data could be obtained from environmental records at the time of online data collection, and the details are presented in Tab. 1.

      Table 1.  Description of IPIX radar data collected in 1993[28,50]

      Data nameWave heights
      (m)
      Wind speed
      (km/h)
      PrimarySecondary
      #172.2998, 10, 11
      #261.1976, 8
      #300.91976, 8
      #310.91976, 8, 9
      #401.0975, 6, 8
      #540.72087, 9, 10
      #2801.61087, 10
      #3100.93376, 8, 9
      #3110.93376, 8, 9
      #3200.92876, 8, 9

      In terms of the Douglas sea state, the last nine sets of data are at sea state 2~3, with a small number of breaking waves and whitecap. Due to the low wave, the target can be illuminated by radar most of the time. The first set of data is at sea state 3~4, and the frequency of breaking waves and whitecap increases, while test targets are sometimes blocked by waves and cannot be directly illuminated by radar. Meanwhile, there is no direct connection between wave height and wind speed. This is because wave height is related to many factors. Large swells can usually spread far from the fully developed sea area where wave height is higher, and short-time strong local wind usually does not influence the wave height, but it can increase the frequency of breaking waves to some extent.

      In 1998, the IPIX radar was located at Grimsby, on Lake Ontario, to collect a new set of data. The radar was set at an altitude of 20 m, and the test target was a floating boat. The range resolution was 30 m, the PRF was 1000 Hz, the dwell time is 60 s, and the number of range cells was 28. The environmental information of the dataset is not published on the websites, and the data information is presented in Tab. 2.

      Table 2.  Description of IPIX radar data collected in 1998[28,50]

      Data nameRange(m)PrimarySecondaryRadar direction
      #2022253201~40112423, 25
      #2025253201~401176, 8
    • The Council for Scientific and Industrial Research (CSIR) radar was deployed at measurement station 3 at the Overberg Test Range (OTB), with location 34°36'56.52" S, 20°17'17.46" E. The plan of the deployment site is shown in Fig. 7, and the location of the deployment site is depicted in Fig. 8. The main characteristics of the deployment site are presented in Tab. 3.

      Table 3.  Main characteristics of OTB MS3[11]

      ParameterValue
      Latitude34°36'55.32"S
      Longitude20°17'20.11"E
      Ground height53 m
      Antenna height56 m
      Distance to sea1.2 km
      Azimuth coverage208°~80° N (SSW-ENE)
      Range(CNR > 15 dB) 1.25~4.50 km
      Grazing angle(<15 km) 3.00°~0.16°
      Grazing angle(CNR > 15 dB) 3.0°~0.7°

      Figure 7.  A plan overview of the deployment site[11]

      Figure 8.  Location of the deployment site in 2006 (OTB)[11]

      The RCS measuring equipment was developed by CSIR and owned by CSIR, the Armaments Corporation of South Africa, and the South African Air Force. It is a calibrated coherent RCS measuring equipment with an operating frequency from 6.5 GHz to 17.5 GHz. The system parameters are presented in Tab. 4.

      Table 4.  Fynmeet system and performance specifications[11]

      System compositionSystem parametersParameter values
      TransmitterFrequency range6.5~17.5 GHz
      Peak power2 kW
      PRF range0~30 kHz
      WaveformsFixed frequency waveform, step frequency waveform, frequency agility waveform
      Agile bandwidth500 MHz pulse to pulse
      AntennaTypeDual-offset reflector
      Gain≥30 dB
      Beamwidth≤2° (3 dB beamwidth)
      Slidelobes≤–25 dB
      ReceiverDynamic range60 dB (instantaneous)/120 dB (total)
      Sensitivity0.1 m2 @ 10 km
      Instrumented range200 m~15 km
      Range gates1~64; ΔR = 100 ns, 300 ns or 400 ns
      Sampler typeIntermediate frequency sampler
      Encoding typeQuadrature
      Image rejection≤–41 dBc

      During the experiment, peripheral recording equipment was deployed as follows: Two weather stations recorded environmental conditions at intervals of 15 min and 1 h, and directional wave-recording buoys recorded important wave heights and maximum wave heights, wave direction, and wave period at intervals of 30 min.

      Three cooperative boats, a WaveRider rigid inflatable boat, a Machann speedboat, and a Timothy fishing vessel (Fig. 9), were deployed on four days during the trial for the recording of boat reflectivity measurements. A series of sea clutter and boat reflectivity measurements was defined, including measurements at different transmit frequencies for different waveforms, azimuth angles, and ranges. This set of measurements was repeated whenever there was a significant change in environmental conditions. In fact, the measurements were repeated once a day. During the planning phase, the entire measurement process took about 6.5 to 7.0 hours, or a full day. Therefore, subsets of the measurements that would take less time to complete were defined (for example, only at a single frequency, at a single azimuth angle). The minimum measurement subset took 2 h to complete.

      Figure 9.  Experimental cooperative boats[11]

      A total of 156 measurement datasets were successfully recorded and preprocessed for sea clutter, totaling more than 160 min. In addition, 113 datasets of boat reflectivity measurements (127 min) were recorded, bringing the total number of datasets recorded during the experiment to 269, and the total recording time was 289 min. Most datasets were recorded with a fixed-frequency waveform. In the subset, most datasets were recorded at 15 m resolution at 9 GHz and 6.9 GHz transmit (Tx) frequencies. Because of the higher average wind speed during the experiment, most datasets were recorded at 165°N antenna azimuth. Whenever possible, measurements were taken at different azimuth angles (usually at 15° intervals). Measurements at other frequencies (8 GHz and 10.3 GHz) and other waveforms were regularly recorded so that the correlation between the sea clutter and boat reflectivity characteristics at different waveforms and the Tx frequency can be investigated.

    • Based on the requirements of sea clutter characteristics and sea-surface target detection technology, the IPIX radar datasets of McMaster University, Canada, and Fynmeet radar datasets of CSIR in South Africa in data acquisition and recording were used for reference. The maritime target detection group of Naval Aeronautical University has launched a “radar-to-sea measured data-sharing program”, which aims to use X-band solid-state fully coherent radar to perform the test in stages and in batches to obtain the measured data and the test auxiliary data under various conditions. The datasets can be constructed to support the cognition of sea clutter characteristics; sea clutter suppression; and the research of sea-surface target detection, tracking, classification, and recognition, which are publicly shared in batches.

      The X-band solid-state power amplifier surveillance/navigation radar, which is mainly used in ship navigation and coast surveillance, was used in the experiment, and it could clearly distinguish various targets in various ranges. The radar has the characteristics of high range resolution, high reliability, and small blind area of detection range (Fig. 10). The solid-state power amplifier combined pulse-transmitting system (see Fig. 11) was adopted to improve the range resolution, reduce the range blind area, and reduce the radar radiation power. The transmitting time was from 40 ns to 100 μs. The distance of the target was calculated by the time difference between the received signal and the transmitted signal. The radar scans 360 degrees in the horizontal plane, and parameters are presented in Tab. 5.

      Table 5.  X-band radar parameters[51]

      Radar parametersParameters setting
      Working bandX
      Frequency range9.3~9.5 GHz
      Measuring range0.0625~96 nm
      Scanning bandwidth25 MHz
      Range resolution6 m
      Pulse repetition frequency1.6 K, 3 K, 5 K和10 K
      Transmit peak power50 W
      Rotating speed of antenna2 rpm, 12 rpm, 24 rpm, 48 rpm
      Length of antenna1.8 m
      Antenna operation modeGaze, circular scanning
      Antenna polarizationHH
      Antenna horizontal beam width1.2°
      Antenna vertical beam width22°
      Information about the dataset and links to its download are available on the website of the Journal of Radar.

      Figure 10.  X-band solid-state power amplifier surveillance/navigation radar[51]

      Figure 11.  Three modes of combined pulse transmission[51]

    • From the published literature, the typical sea clutter measurements of shore-based radar in foreign countries include the multi-bands (L, S, and X band) sea clutter measurements of the Australian Defense Science and Technology Organization[52-54], the X-band sea clutter measurements of the Naval Research Laboratory[55], and the Ka-band high-resolution sea clutter measurements of the south coast of Spain[56]. Others are the multi-band sea clutter measurements (L, S, C, X, and Ku bands) of the Naval Air Warfare Center[57,58], the multi-band sea clutter measurements (S, X, and Ku bands) of the south coast of England[59], the X-band sea clutter measurements of Japan[60], the Mediterranean RFC (refractivity from clutter) and sea clutter environmental experiment (C, X, Ku, Ka, and W bands) of Germany and France[61], and the S-band NetRAD sea clutter measurements of the University College London[62-66].

    • As earlier mentioned, with the refinement of radar observation, background clutter and target returns have become so complex that it is difficult to construct an accurate statistical model. In this case, the “double-high” system with high spatial resolution and high Doppler resolution is the main technical approach. However, the high-resolution radar faces the problem of extremely complex high-resolution sea clutter characteristic and the small-target return characteristic; moreover, the key to the performance of sea clutter detection lies in the deep cognition, elaborate perception, and full utilization of sea clutter characteristics. This section focuses on the development and dynamics of feature-based detectors for high range resolution and long-time-observation radars. This paper mainly deals with signal-level features, not track-level and data-level features, so data-level features will not be introduced.

      Existing features of feature-based detection methods are listed in Tab. 6. A fractal feature is a single feature used in early feature-based detection, and it has a good detection performance for SFTs on sea surface under long-term observation[29,31,67]. Moreover, numerous scholars have studied multifractal features[30,68], fractal features in different transform domains[69-73], and obtained good detection results. The chaotic characteristics of sea clutter suggest that the time series of sea clutter can be described and predicted by nonlinear dynamic models[33], and the prediction error is used to make a judgment. To eliminate the influence of texture component non-stationarity in the compound Gaussian model under long-term observation, the speckle consistency factor feature[74], which belongs to the time domain, is proposed based on the compound Gaussian model. The single-feature-based detection compares the computed feature values with the detection threshold under the given false alarm probability to judge the presence or absence of the target. With the development of feature-based detection methods, numerous features in different domains have been proposed, for example, features based on energy differences in the time domain and spectrum differences in the frequency domain[28], micro-Doppler features suitable for micro-motion targets based on TF analysis[41,42], features based on the difference between normalized TF distributions of sea clutter[75], and polarization features[76]. Tab. 6 presents some common features. With the increase in the number of features, single-feature-based detection will cause a certain performance loss, while multi-feature-based detection is the general trend. However, how to determine the detector threshold in multi-feature-based detection becomes a difficult problem. In Ref. [28], our group proposes a convex hull learning algorithm that treats the detection problem as a single classification problem and transforms the determination of the detection threshold into the selection of the detection decision region, and it is only suitable for the 3D feature space. With the increase in feature dimension, the complexity of the convex hull learning algorithm is increased, and it is difficult to realize detection. Also, machine learning-based algorithms have been proposed. In Refs. [77,78], Support Vector Machine (SVM) and decision tree algorithms were used to determine the decision region, and good detection performance was obtained. The following is a detailed description of several features and detection methods.

      Table 6.  Features introduction of feature-based detection method

      Existing features
      Fractal featuresSingle fractal features[29,31,67], multifractal features[30,68], fractal features in FRFT[69-73]
      Chaotic characteristics of sea clutterCorrelation dimension, Lyapunov exponent, and Kolmogorov entropy[33]
      Features in the time domainRelative average amplitude[28], temporal information entropy[77], temporal Hurst exponent[77], the speckle consistency factor[74]
      Features in the frequency domainRelative Doppler peak height[28], relative vector-entropy[28], frequency peak-to-average ratio[77], Hurst exponent in frequency domain[78]
      Features in the time and frequency domainsMicro-Doppler features[41,42], the ridge integration of NTFD[75], the number of connected regions and the maximum size of connected regions in a binary image[75]
      Polarization featuresRelative surface scattering power[76], relative dihedral scattering power[76], and the relative volume scattering power[76]
    • Limited by the dimension of detector design, the early detection methods of small targets on sea surface are mainly based on single feature, and as a result, it is easy to design the detector and determine the detector threshold. Therefore, single-feature-based detection has been the focus of scholars. Scholars at home and abroad have proposed many effective feature-based detection methods in different domains.

      The fractal theory was developed by Mandelbrot in the 1970s to describe irregular natural features that could not be analyzed by the traditional Euclidean geometry, including mountains, rivers, lightning, coastlines, galaxies to snowflakes, ice crystals, leaves, and the patterns and complex processes of natural formation; The fractal theory constitutes an active branch of nonlinear science[79,80]. Fractals can be used to describe natural phenomena or substances with certain self-similarity at different scales, from macroscopic to microcosmic. The sea surface is undulating, and the large-scale swell with different wavelengths also has some fractal characteristics. D. L. Jaggard[81,82] found that the reflection of light and electromagnetic waves from rough surfaces with fractal characteristics is also fractal. G. Franceschetti[83] further described electromagnetic echoes from naturally rough surfaces as two-dimensional fractal Brownian motion. According to the analysis of the sea surface and sea clutter, F. Berizzi[84] further pointed out that sea clutter satisfies fractal characteristics to a certain extent and has the same fractal dimension as sea surface. On this basis, F. Berizzi[85] utilized the fractal method to generate sea clutter; this method can describe the relationship between sea clutter and sea state or radar and overcomes the shortcoming that the traditional method of generating sea clutter is independent of operating conditions and environment; at the same time, the fractal characteristics of sea clutter are retained in the generated clutter.

      In 1993, T. Lo[29] found that the fractal dimension of sea clutter is about 1.75, and when there is a target signal in sea clutter, the fractal degree of the sea surface is reduced; therefore, based on the fractal theory, a new target detection method has been proposed. Fractal Brownian motion is the simplest single fractal model. Because the fractal Brownian motion still has the fractal characteristic after the Fourier transform, based on the result that the sea clutter amplitude echo sequence is modeled as the fractal Brownian motion, the fractal characteristic of the measured sea clutter in the frequency domain has been studied, and an attempt has been made to detect sea-surface targets[31,67]. In 2002, the characteristics of amplitude distribution and space-time correlation of sea clutter were analyzed by Gao[68], and it was theoretically proved that the measured sea clutter was multifractal. In 2006, Hu Jing[30] introduced a sea-surface target detection algorithm based on the multifractal theory. According to the analysis of the measured sea clutter data, it was found that the sea clutter data have multifractal characteristics in the range of 0.01 s to several seconds. By calculating the Hurst exponent of sea clutter, the proposed target detection algorithm was found to have good detection performance. The detection method is as follows:

      (1) The received sea clutter time series is ${{x}} = \left\{ {\left. {{{x}}\left( i \right)} \right|i = 1,2, ··· ,N} \right\}$, which is regarded as a random walk model.

      (2) If the following relation is satisfied, the sea clutter sequence is a fractal process.

      $${F^{\left( q \right)}}\left( m \right) = {\left\langle {\left| {{{x}}\left( {n{\rm{ + }}m} \right) - {{x}}{{\left( n \right)}^q}} \right|} \right\rangle ^{1/q}}\sim {m^{H\left( q \right)}}$$ (4)

      where H(q) is a function of real-valued q.

      (3) ${F^{\left( q \right)}}\left( m \right)$ and ${m^{H\left( q \right)}}$ are drawn in double logarithmic coordinates. In the approximate linear region, the slope H(q) is found through line fitting.

      (4) When H(q) does not change with q, the sequence is a single fractal; otherwise, it is considered as a multifractal sequence. When q = 2, it is usually called Hurst exponent. By comparing the values of the Hurst exponent, a fractal-based detector is constructed based on the Hurst exponent.

      (5) If the Hurst exponent of the sequence is larger than the set threshold, the target can be detected.

      The detection performance of the detector can be verified by the measured sea clutter data. In Fig. 12, the solid line represents the echo of the pure sea clutter range cells when q = 2, and the hollow line is the radar return of the target cell and the secondary cells around the target cell when q = 2. From Fig. 12 (a), in the 24~212 interval, i.e., 0.08~4.00 s, the F(2) trend is approximately linear. It can be considered that the sea clutter data in this interval has a fractal structure, and this interval is called the scale-free region. The slope H(2) of the straight line, i.e., the Hurst exponent, is obtained by fitting the curve in the scale-free region using the least square method. As shown in Fig. 12(b), the Hurst exponent of the radar returns in the target range and secondary ranges is significantly higher than that of the pure sea clutter. Therefore, the Hurst exponent can be used for the detection of sea-surface targets.

      Figure 12.  Analysis of fractal characteristics in 14 range cells[30]

      The relationship between H(q) and q is demonstrated in Fig. 13. Similar to Fig. 12, the solid line represents the pure clutter range cells, and the hollow line represents the target cell and the secondary cells. It can be seen that H(q) is a function of q, from which it can be concluded that the sea clutter has multifractal characteristics; especially, when the radar returns contain targets, H(q) changes more dramatically with q.

      Figure 13.  Trends of H(q) in 14 range cells[30]

      The target detector is constructed by the Hurst exponent, and the Hurst frequency distribution of 10 sets of sea clutter data of the IPIX radar in the HH polarization channel in 1993 is illustrated in Fig. 14.

      Figure 14.  Hurst frequency distribution of clutter cells and target cells under HH polarization[30]

      In Fig. 14, the solid bars represent the target cells, and the hollow bars represent the pure clutter cells. It can be seen that the Hurst exponent of the pure clutter and the target returns can be completely separated under the measured data in HH polarization. This method is also ideal for other polarization channels. However, the observation time of each range cell is about 131 s in the datasets, and the detection performance of this method decreases rapidly with the shortening of the observation time.

      The nonlinear fractal-based detector in the time domain is also extended to the other transform domain. The fractal properties in the FRFT domain are also a hot topic. The multifractal characteristics of sea clutter FRFT spectrum have been studied in Refs. [69,70] and verified by measured data. In Ref. [71], a method based on the difference of spatial fractal features in the FRFT domain is proposed to detect weak targets on sea surface. The multiscale Hurst exponent is calculated based on the FRFT spectrum of sea clutter in Ref. [72]. Compared with the traditional multiscale Hurst exponent, the target detection performance is effectively improved. Recently, a team from Nanjing University of Posts and Communications developed a fractal-based detector in the FRFT domain[73], which analyzes the difference between sea clutter and SFTs based on the fractal features in the FRFT domain. The fractal curves of the target and sea clutter were obtained by detrended fluctuation analysis; the fractal dimension and the fractal dimension variance were extracted from the appropriate scale-invariant interval; and the detection analysis of the two features was completed by the convex hull learning algorithm. The effectiveness of the proposed algorithm compared with that of the single-feature-based detector and CFAR detectors was verified.

      Fig. 15 is the flowchart of a fractal-based detector in the FRFT domain. The fractal dimension and fractal dimension variance of sea clutter and target in the FRFT domain were studied, and Fig. 16 is the fractal curve determined by detrended fluctuation analysis. Fractal dimension and fractal dimension variance were obtained by selecting a scale-invariant interval. Fig. 17 is the distribution of training samples and test samples on the two-dimensional feature plane when the false alarm ratio is 10–3 and 10–2. Fig. 18 compares the performances of the two-feature-based detector in the FRFT domain and CFAR detectors.

      Figure 15.  Flowchart of fractal-based detector[73]

      Figure 16.  Fractal curves[73]

      Figure 17.  Distribution of training samples and test samples on the two-dimensional feature plane[73]

      Figure 18.  Classic CFAR algorithm detection curves[73]

      Based on the fractal characteristics of sea clutter, a series of detection algorithms based on fractal theory has been developed in recent years, including single-fractal features, multifractal features[86,87], multifractal correlation spectrum[88], higher-order fractal features, extended self-similarity, and the theory of modulus and fractal. With the development of the fractal theory, more and more detection methods are being developed, and these methods have become an active research direction in target detection.

    • The sea surface is a very complex dynamical system, and the radar echo is influenced by several factors, including the wind direction, wave height, wave direction, and ocean current. In 1995, Professor Haykin[33] applied correlation dimension, Lyapunov exponent, and Kolmogorov entropy and indicated that sea clutter has finite correlation dimension; its largest Lyapunov exponent is positive, and sea clutter is short-term predictable. The correlation dimension and the largest Lyapunov exponent are both necessary for the time series components and do not change significantly with the sea state or geographical locations. These results indicate that there may be a general chaotic structure that leads to sea clutter generation. In conclusion, the sea clutter time series has significant chaotic characteristics. According to the short-term predictability of sea clutter, a nonlinear prediction model has been constructed for sea clutter, and the prediction error was used to test the statistical hypothesis[89-93]. Based on the chaos theory model, the sea clutter time series can be described and predicted by some nonlinear dynamic models. Therefore, the sea clutter time series can be nonlinearly fitted by the neural network learning method, and target detection is accomplished by prediction error. Chaotic systems are extremely sensitive to noise and feature the “butterfly effect”. However, in actual systems, they are affected by various noises[94], which lead to system anomalies and instability. The sea clutter chaotic model was once controversial in the academic world. Haykin proposed a modification to the previously proposed sea clutter chaotic model. It is believed that the sea clutter may be generated by random chaos, multiple deterministic chaoses, or a mixture of both, and an increasing number of scholars point out that sea clutter does not have chaotic characteristics[95-97]. However, the idea of anomaly detection based on neural network and SVM has become an idea of feature-based detection research. The main idea of the detection algorithm based on the sea clutter chaotic model is illustrated in Fig. 19.

      Figure 19.  Flowchart of detector based on neural network prediction[33]

      Here, ${{{x}}_{\rm{A}}}\left( n \right)$ is the actual received signal, and ${\bar {{x}}_{\rm{A}}}\left( n \right)$ is the prediction signal based on the neural network. When ${{{x}}_{\rm{A}}}\left( n \right)$ only contains the sea clutter, the output prediction error $ e\left( n \right) $ is small, but when the target returns exist in ${{{x}}_{\rm{A}}}\left( n \right)$, the output $e\left( n \right)$ will be a larger value. Based on this, we can get the target detection algorithm based on the chaos model as follows:

      (1) For a radar echo ${{{x}}_{\rm{A}}}\left( n \right)$, $n = 1, ··· ,{N_{\rm{T}}}$, the prediction value ${\bar {{x}}_{\rm{A}}}\left( n \right)$ is obtained by the neural network model; then the prediction error is calculated as $e\left( n \right) = {{{x}}_{\rm{A}}}\left( n \right) - {\bar {{x}}_{\rm{A}}}\left( n \right)$.

      (2) The cumulative mean square error of the prediction in the observation time is calculated as,

      $$ {E_e} = \frac{1}{{{N_{\rm{T}}}}}\sum\limits_0^{{N_{\rm{T}}}} {{e^2}\left( n \right)} $$ (5)

      where ${N_{\rm{T}}}$ is the signal length in the observation time.

      (3) Comparing $ {E_e} $ with the threshold of the given false alarm probability, if the threshold is exceeded, the target exists and the H1 hypothesis is considered valid; otherwise, the target does not exist and the H0 hypothesis is considered valid.

      Fig. 20 displays the Receiver Operating Characteristic (ROC) curves of the detection results, where the solid line represents the traditional Doppler CFAR detector, and the broken line represents the neural network-based detector. It can be seen that the detection performance based on the neural network algorithm is better than that of the traditional Doppler CFAR detector.

      Figure 20.  ROC curves of neural network-based detector and traditional Doppler CFAR detector[33]

      By learning the sea clutter time series, the neural network-based detection method can fit the sea clutter time series well, so that the sea clutter time series can be predicted and the target can be detected by prediction error. The flowchart is shown in Fig. 21. The learning rules of neural network for sea clutter time series can be understood as the establishment of complex nonlinear mapping by neural networks. All sea clutter features are the learning objects of the neural network; therefore, the detection method based on the prediction error of the network is based on the full features of sea clutter. However, the method based on full features has many disadvantages. The amounts of common features of sea clutter and target are more than different features; thus, this method consumes a large amount of neural network resources, and the learning scale of the network is increased, and the learning efficiency is reduced. The over-large network makes the learning process train many fine features, resulting in the “overfitting” phenomenon, and the network has poor generalization ability. Therefore, neural network-based learning has been neglected by researchers for a long time.

      Figure 21.  Flowchart of detection methods based on prediction

      However, with the development of deep learning and artificial intelligence, the neural network-based learning method has good application in radar target detection[98]. Typical deep learning networks include CNN, Sparse AutoEncoder (SAE), and deep belief network. They are widely used in Synthetic Aperture Radar (SAR) images and produce good results, and CNN is the most widely used. A CNN mainly consists of a convolution layer, pooling layer, and fully connected layer. The convolution layer extracts the low-level to complex features of the input image. In the pooling layer, the image is oversampled to reduce the feature dimension and control the overfitting. The fully connected layer arranges the features obtained from the previous layer and connects them with the neural network. The SAE method can extract features without supervision; thus, it has become the research focus. The convolutional autoencoder can be obtained by combining CNN and SAE, and it has good performance in practical applications. The emergence of deep learning networks leads to a new direction of sea-surface target detection. Based on the micro-motion characteristics of sea-surface targets, some scholars have proposed to analyze and process the micro-Doppler spectrum of sea-surface targets by CNN, which achieves better detection performance than the traditional methods[43]. An increasing number of scholars are trying to utilize deep learning networks to detect and classify sea-surface targets, and the application of neural networks in sea-surface target detection is still developing and expanding.

    • To improve the range resolution and detection range, modern radars usually transmit signals with large time-bandwidth product. Linear Frequency Modulation (LFM) signals can be easily produced through both analog and digital techniques, and their form is simple; therefore, the research on LFM signals is more in-depth. The TF analysis method can provide better joint distribution information in the time domain and the frequency domain; it is widely used in signal processing and has become a powerful tool for the analysis of time-varying non-stationary signals. When the target is moving at a constant acceleration or deceleration relative to the radar, the target echo is an LFM signal due to the Doppler effect. Scholars have used several TF analysis tools such as the STFT, FRFT, Wigner-Hough transform, pseudo-Wigner-Ville distribution, Radon-Wigner transform[99,100], Radon-ambiguity transform[101], and chirplet transform[102]. The detection of LFM signals in non-stationary backgrounds is accomplished by parametric and non-parametric methods[103].

      According to the characteristics of ship wakes in SAR images, Radon transform and Hough transform are applied to detect ships on sea surface[104,105]. In these method, the observed area needs to be imaged, and the detection performance is closely related to the image quality. Given the non-uniformity and non-stationarity of sea clutter, many detection methods based on TF distribution have been proposed for target detection[106-111]. Due to the differences between sea clutter and target in TF clustering and duration, some researchers have used TF iterative decomposition algorithms for target detection[112,113]. The FRFT can be regarded as a rotation operation on the TF plane, and it has no cross term; thus, it is very suitable for processing LFM signals. Some scholars have applied the FRFT to the detection of uniformly accelerated targets on sea surface[114-116]. However, the sea-surface targets are usually affected by the sea waves, and the target signal is a nonlinear FM signal, which makes the detection performance of this kind of detection method in practical applications have a certain decline. To improve the detection performance of sea-surface targets, in recent years, scholars have proposed new detection methods based on TF analysis[35-40]. In Ref. [35], Haykin transformed the problem of target detection in the sea background into the problem of double classification in pattern recognition and used principal component analysis to extract TF features from a TF plane. The clutter and target classification was accomplished by a neural network algorithm. However, in practice, radar needs to detect unknown targets, so the characteristics of targets cannot be obtained exactly, which limits the practical application of this method. Nonetheless, the method in which the target detection problem is transformed into a double classification problem provides a new idea for the detection of SFTs on sea surface. Because of the excellent TF localization characteristics, wavelet transform can be adopted to observe different details in radar echoes; thus, some scholars try to use wavelet transforms to detect sea-surface targets[117]. In recent years, many scholars have proposed target detection methods based on deep neural networks, the micro-Doppler theory, and TF maps; these methods also belong to the category of TF analysis.

      The micro-Doppler theory has been a hot spot in the field of target detection in recent years. Based on the analysis and modeling of the echo of a micro-motion target, the micro-motion features of the target are extracted and detected by different detection methods. The micro-Doppler theory has been introduced into the detection of dim targets on the sea surface, and good detection results have been obtained. Based on the micro-Doppler theory, the Naval Aeronautical University team proposed a sea-surface micro-motion target detection method based on GSTFRFT[41], a radar target micro-motion feature extraction and detection method based on ST-STFD[42], and a sea-surface micro-motion target detection and classification method based on CNN[43].

      (1) Sea-surface micro-motion target detection method based on GSTFRFT[41]: In Ref. [118], the modeling of the micro-motion features of a rigid body target on the sea surface is introduced, and the micro-motion target models under the conditions of short-term observation and long-term observation were studied. The motion of a target on the sea surface is a compound motion in the 3D space, which can be characterized by the non-uniform motion and the three-axis rotation of the target in translational motion. In Ref. [41] the model of the sea-surface micro-motion target was applied under short-time observation; the target could be modeled as an amplitude fluctuation FM signal, and the target returns in the same range cell could be modeled as

      $$ \begin{split} {{x}}\left( t \right) \,& = {{s}}\left( t \right) + {{c}}\left( t \right) \\ & = \sum\limits_i {{{{A}}_i}\left( t \right)\exp\left( {{\rm{j}}2\pi {f_i}t + {\rm{j}}\pi {k_i}{t^2}} \right) + {{c}}\left( t \right),\left| t \right| \le {T}} \end{split} $$ (6)

      where ${{{A}}_i}\left( t \right)$ is the amplitude of the first i micro-motion signal, ${f_i}$ represents the central frequency, and ${k_i}$ is the FM. At the high sea state, the sea spike presents the micro-Doppler characteristic, and the radar will often misjudge the sea spike as the target. Therefore, the method of sea spike identification based on three characteristic parameters proposed by Fred Posner[119] was used to suppress the sea spike before the research of the micro-motion target. The time series of sea clutter extracted from sea spike were calculated by GSTFRFT, and the transformed amplitude was used as the test statistic and compared with the detection threshold under the given false alarm probability, to judge the existence of the target. Fig. 22 displays the features after the sea clutter suppression in the transform domain, and Fig. 23 compares the results of the methods based on STFT and GSTFRFT after sea clutter suppression. In the GSTFRFT domain, the energy of the micro-motion signal is accumulated, and the peak value is sharp and the side lobe is low. Because of the sea spike suppression, the SCR and the detection probability are improved. Compared with the traditional MTD, GSTFRFT can improve the detection performance by more than 30%.

      Figure 22.  Micro-motion signal features after sea clutter suppression (N = 256)[41]

      Figure 23.  Comparison of micro-motion target detection results based on STFT and GSTFRFT after sea clutter suppression (N = 512)[41]

      (2) Radar target micro-motion feature extraction and detection method based on ST-STFD[42]: In Ref. [42], several common TF distributions of the micro-motion signals expressed by Eq. (6) were selected to analyze the signals from the sea surface; the distributions were the STFT distribution, WVD, SPWVD, and FRFT. Among them, the TF resolution of the STFT was poor, and the WVD had a serious cross term. It was difficult for SPWVD to consider the high-resolution instantaneous FM of the micro-motion signal, while for FRFT as a parameter searching method, the accuracy of parameter estimation was limited by the TF resolution. Given the difference between micro-motion signals and sea clutter, using the idea of sparse decomposition, the Sparse TF Distribution (STFD) was introduced to sea-surface target detection, and two micro-motion feature extraction and detection methods based on ST-STFD were proposed, which are ST-SFT and ST-SFRFT. Fig. 24 and Fig. 25 show the detection results of the ST-SFT-based and ST-SFRFT-based detection methods. Tab. 7 compares the performances of different methods for the detection of micro-motion targets. The detection performance of ST-STFD method was significantly better than that of the traditional TFD method, because the ST-STFD method was designed in the optimal sparse domain of the micro-motion signal, but the efficiency of the ST-STFD method still needs to be improved.

      Table 7.  Detection performances of different methods for the detection of micro-motion maritime targets[42]

      ParameterMTDFRFTWVDSPWVDST-SFTST-SFRFT
      Pd(%) (SCR = –5 dB)39.2657.2635.6855.2449.2171.35
      Pd(%) (SCR = 0 dB)52.8476.8462.2772.5863.2885.69

      Figure 24.  ST-SFT-based micro-motion target detection results (starting time = 20 s)[42]

      Figure 25.  ST-SFRFT-based micro-motion targets detection results (starting time = 20 s)[42]

      (3) Detection and classification method of sea-surface micro-motion targets based on CNN[43]: With the rapid development of deep learning, more intelligent methods are being applied to sea-surface target detection. Based on the time-varying characteristics of micro-motion targets, the TF image can be used to effectively analyze the targets. In Ref. [43], the TF images of radar echoes were detected and classified by CNN, and good results were obtained. Common CNNs are LeNet, AlexNet, and GoogleLeNet. LeNet was first used for digit recognition; AlexNet is deeper than LeNet and can learn more complex objects; and GoogleLeNet increases the depth and width of neural networks, and it guarantees the invariability of computing resources. The training set and the test set of target detection in Ref. [43] were divided into clutter and target, and the TF image obtained from the measured sea clutter data was used as the training set and the test set. The simulated target signal of a certain SCR based on a sea-surface moving target model was regarded as the target training set and the test set. First, the CNN model was trained by the training set, and then the test set was detected by CNN. The three CNN models had the same signal-processing efficiency, among which the LeNet model had the highest detection probability and the AlexNet model had the lowest false alarm probability. Fig. 26 is the processing flow diagram of the CNN-based method, and the results are presented in Tab. 8. Compared with the traditional SVM method, the CNN-based method had a higher detection probability and lower false alarm rate, but the latter was easily affected by the SCR.

      Table 8.  Detection results of different models(%)[43]

      ModelLeNetAlexNetGoogLeNet
      False alarm ratio1.240.040.24
      Detection probability92.2884.4490.94

      Figure 26.  Processing flow diagram of CNN-based method[43]

    • In recent years, considering the difference between the features of clutter and target, some scholars have proposed speckle stationarity as a feature to detect targets on sea surface[74]. Sea clutter is non-stationary, which is embodied by the non-stationary texture of sea clutter, and the speckle of sea clutter is approximately stationary within a certain time. The non-stationarity of the texture is disadvantageous to the detection of sea-surface targets, and the texture non-stationarity needs to be first eliminated. Therefore, the speckle is first extracted from the non-stationary sea clutter, and then the test statistic is designed based on the speckle characteristics. Because of the strong structure of the SFT, the speckle structure is not as obvious as that of the SFT; that is, the consistency of the speckle is weaker than that of the target. Therefore, the speckle consistency is proposed as a feature to detect SFTs on sea surface. The flowchart of the detection is displayed in Fig. 27.

      Figure 27.  Flowchart of a feature-based detector using the average speckle consistency factor[74]

      First, using the reference samples ${{{r}}_{q,k}}$, we estimate the Normalized Samples Variance Matrix (NSCM) and obtain the NSCM estimation ${{{M}}_q}$, where q is the interval, K is the number of reference cells, and N is the number of cumulative pulses.

      $$ {{{M}}_q} = \frac{N}{K}\sum\limits_{k = 1}^K {\frac{{{{{r}}_{q,k}}{{r}}_{q,k}^H}}{{{{r}}_{q,k}^H{{{r}}_{q,k}}}}}, \quad q = 1,2,··· ,Q $$ (7)

      Eq. (8) standardizes the results of Eq. (7), and ${\bar {{M}}_q}$ is obtained; tr(·) represents the trace of the matrix.

      $$ {\bar {{M}}_q} = \frac{{N{{{M}}_q}}}{{{\rm{tr}}({{{M}}_q})}},\quad q = 1,2, ··· ,Q $$ (8)

      Then, by the normalized value ${\bar {{M}}_q}$, the speckle consistency factor is calculated according to Eq. (9), where τ is the time interval and $ {\Vert \cdot \Vert }_{2}$ is the 2-norm of the matrix:

      $$ \begin{split} & \rho (\tau ) = \frac{1}{{Q - \tau }}\sum\limits_{q = 1}^{Q - \tau } {\frac{{{{\left\| {{{\bar {{M}}}_q} - {{\bar {{M}}}_{q + \tau }}} \right\|}_2}}}{{{{\left\| {{{\bar {{M}}}_q}} \right\|}_2} + {{\left\| {{{\bar {{M}}}_{q + \tau }}} \right\|}_2}}}} \\ & \qquad \tau = 1,2, ··· ,Q - 1 \end{split} $$ (9)

      Finally, the average consistency factor of the speckle is obtained as

      $$\rho = \frac{1}{{{\tau _2} - {\tau _1}}}\sum\limits_{\tau = {\tau _1}}^{{\tau _2}} {\rho (\tau )} $$ (10)

      where $[{\tau _1},{\tau _2}]$ is the average time interval. The speckle consistency reflects the time similarity of the covariance matrix of the speckle and further reflects the speckle structural stationarity. The smaller the average consistency factor, the stronger the time stationarity of the speckle. The structure of the SFT is stronger than that of the sea clutter; therefore, the average consistency factor of the speckle of the returns with SFTs is smaller than that of the pure sea clutter.

      Fig. 28 shows the distribution of the speckle consistency factor under four polarization channels. Fig. 29 compares the performances of the speckle consistency factor detectors from the measured data.

      Figure 28.  Average consistency factors of pure clutter and clutter with target under four polarization channels[74]

      Figure 29.  The detection probabilities of the four detectors under four polarization channels, L = 1024[74]

    • With the further improvement of radar resolution and the increase in dwell time, many new signal-processing methods have been applied, resulting in a variety of features that can effectively detect targets. Multiple features can complement each other for different types of targets, which can greatly improve the detector robustness. With the successful design of the new multi-feature detector and the gradual improvement of the signal-processing hardware level, multi-feature detection methods based on feature engineering or artificial intelligence self-selection are becoming increasingly mature, and they have become an important research direction of sea-surface target feature-based detection.

    • In feature-based detection, feature extraction is very important. The ability to distinguish target and clutter largely determines the detector performance. The feature can be extracted from the different digital features of the target and clutter. From the traditional test statistics used in the traditional radar target detection and the overall recognition of the target return characteristics, different features are extracted according to cognitive conclusions. If the radar transmits a coherent pulse of length N at a wave position and receives it through the I/Q channel, the complex data of each range cell can be obtained. Then, the problem of radar target detection can be expressed as the binary hypothesis problem of Eq. (11):

      $$\left. \begin{aligned} & { {H_0}:\left\{\!\!\! {\begin{array}{*{20}{l}} {{{x}}\left( n \right) = {{c}}\left( n \right),\quad n = 1,2, ··· ,N } \\ {{{{x}}_p}\left( n \right) = {{{c}}_p}\left( n \right),\quad p = 1,2, ··· ,P} \end{array} } \right.} \\ & {{H_1}:\left\{\!\!\! {\begin{array}{*{20}{l}} {{{x}}\left( n \right) = {{s}}\left( n \right) + {{c}}\left( n \right),\quad n = 1,2, ··· ,N } \\ {{{{x}}_p}\left( n \right) = {{{c}}_p}\left( n \right),\quad p = 1,2, ··· ,P } \end{array}} \right.} \end{aligned}\!\!\!\!\right\} $$ (11)

      where x(n) is the time series received by the cell under test(CUT), s(n) is the time series received from the target returns, c(n) is the time series received from the sea clutter, N is the number of accumulated pulses, and P is the number of reference cells around the CUT. The H0 hypothesis indicates that the radar returns only contain sea clutter and noise, and the H1 hypothesis indicates that the radar returns contain targets. A large number of pure clutter data can be obtained from the received radar returns, so the feature vectors ${{{\xi}} _i}$ of pure clutter can be extracted, which can form training samples of pure clutter ${{{L}}_{{H_0}}} = \{ {{{\xi}} _i} \in {R^3}:i = 1,2, ··· ,I\}$. Because of the sea targets complexity and diversity, it is difficult to obtain the target returns and the feature vectors of all kinds of targets when the targets are included in the radar returns, and consequently, training samples of targets cannot be formed. Therefore, the decision region can be obtained by the training samples of pure clutter, and the target existence can be judged by judging whether the feature vector of the radar returns of the CUT falls into the decision region. If it does not fall into the decision region, the target does not exist; otherwise, the target exists.

      (1) Detection method based on original tri-features[28]: Although fractal-based detectors have good detection performance in the case of long-time observation, in practical sea-surface target detection, the observation condition cannot reach the ideal long time, and fractal-based detectors cannot obtain the ideal effect in practical applications. To mitigate the problem of fractal-based detection methods, a tri-feature-based detection method for SFTs has been proposed[28]. The main idea of this method is to detect SFTs on the sea surface by using three complementary features (the relative average amplitude, relative Doppler peak height, and relative vector-entropy) to distinguish targets and clutter, and to transform the detection problem into a single classification problem in the feature space; then, a fast convex hull learning algorithm is used to determine the decision region for target detection.

      In radar echoes, three kinds of features with separability are extracted from the time domain and the frequency domain. The energy information in the time domain can be used as a mark to distinguish the target and clutter, and the differences in the frequency spectrum can also be used to distinguish the target and clutter. The relative average amplitude, relative Doppler peak height, and relative vector-entropy can be extracted from these differences. Fig. 30 shows the distributions of features in a 3D feature space, under different observation times: 0.512 s, 1.024 s, 2.048 s, and 4.096 s. With the increase in observation time, the separability of target and clutter and the detection performance improved.

      Figure 30.  Distributions of features of clutter-only vectors and vectors with target in 3D feature space

      The fast convex hull learning algorithm reduces the optimal detection decision region to the convex hull region of Eq. (12) under the given false alarm rate

      $$ \begin{split} & \mathop {\min}\limits_\varOmega \left\{ {{\rm{Volume}}\left( \varOmega \right)} \right\} \\ \\ & {\rm{s.t.}}\frac{{\# \{ i:{{{\xi}} _i} \in \varOmega \} }}{I} = 1 - {P_{\rm{F}}} \end{split} $$ (12)

      where $\# {\rm{A}}$ represents the number of elements of set A, ${P_{\rm{F}}}$ is the false alarm ratio, and I is the total number of pure clutter feature vectors. Fig. 31 illustrates the convex hull generated from the original clutter-only data and the convex hull with a given false alarm probability.

      Figure 31.  Convex hull with the original training data and convex hull with given false alarm rate

      The procedure of the tri-feature-based detector is divided into two parts: training and detection. In the training part, we need to collect the clutter samples and extract the pure clutter features. Then, the fast convex hull learning algorithm is used to train the detection decision region that satisfies the false alarm ratio, such as in Fig. 31. In the detection part, features of test samples in the CUT need to be extracted, and the existence of the target is judged by whether the test sample feature falls in the decision region.

      (2) SVM-based sea-surface small-target detection[77]: With the development of feature-based detection, more and more effective features are being used to distinguish sea clutter from target returns. The problem of sea-surface target detection can be transformed into a special binary classification problem in the feature space by effective feature extraction. There are many excellent machine learning algorithms to solve the binary classification problem, such as SVM and neural networks. However, the core of the traditional machine learning algorithm is to search a classification plane, which can make the error rate of the two classes balanced and minimum. In radar target detection, the requirement of the false alarm rate is much higher than that of the false dismissal probability (usually, the false dismissal probability can be up to a few tenths, but the false alarm is generally less than 10–3). Therefore, the key problem of combining machine learning algorithm with target detection is how to accurately control the false alarm rate. In 2019, Li et al constructed an SVM-based sea-surface small-target detection method by combining SVM with radar target detection; the method not only realized controllable false alarm rate but also had better detection performance than other detectors.

      In this SVM method, first, temporal information entropy, temporal Hurst exponent, and the frequency peak-to-average ratio are extracted from the received sea clutter time series, and they are used to construct a three-dimensional feature vector ${{{F}}_i}$. The target (+1) and sea clutter (–1) are labeled as ${y_i}$= {+1, –1}; then, M labeled training samples can be represented as $\left\{ \left( {{{{F}}_i},{y_i}} \right), i = 1, 2, ··· ,M \right\}$. In this feature space, the feature vectors of the target and the clutter are linearly inseparable. To solve this problem, a nonlinear kernel function can be used to map a three-dimensional feature vector ${{{F}}_i}$ to a higher-dimensional feature space and to convert linearly inseparable data into a separable one, and an SVM-based detector can be constructed using radial basis function as a kernel function.

      $$k\left( {{{{F}}_1},{{{F}}_2}} \right) = \exp\left( { - \frac{{\left\| {{{{F}}_1} - \left. {{{{F}}_2}} \right\|} \right.}}{{2{\delta ^2}}}} \right)$$ (13)

      After the mapping to a high-dimensional space, the next step is to find a linearly separable hyperplane ${w^{\rm{T}}}{{F}} - b = 0$ that can separate the target from the clutter in the high-dimensional feature space. The parameters $w$ and $b$ can be determined by solving the following optimization problem

      $$ \begin{split} & \mathop {\min}\limits_{w,b,{{\xi}} } \frac{1}{2}{\left\| w \right\|^2} + \sum\limits_{i = 1}^M {\left( {\frac{{1 - {y_i}}}{2}{b_0} + \frac{{1 + {y_i}}}{2}{b_1}} \right){{{\xi}} _{{i}}}} \\ & {\rm{s.t.}} \;\;{\rm{C}}1:{y_i}[k(w,{{{F}}_i}) - b] \ge 1 - {{{\xi}} _i},i = 1,2, \cdots ,M \\ & \qquad {\rm{C}}2:{{{\xi}} _i} \ge 0,i = 1,2, ··· ,M\\[-10pt] \end{split} $$ (14)

      where ${{{\xi}} _i}$ is the slack variable. The false alarm ratio can be controlled by controlling the change of β0 and β1. According to the theoretical basis of SVM, Eq. (14) can be solved by sequential minimal optimization. After the hyperplane ${{{w}}^{\rm{T}}}{{F}} - b = 0$ is obtained, when ${{{w}}^{\rm{T}}}{{F}} - b > 0$, ${y_j} = {\rm{ + }}1$ is identified as the target. When ${{{w}}^{\rm{T}}}{{F}} - b \le 0$, ${y_j} = - 1$ is identified as the sea clutter. The results are presented in Tab. 9.

      Table 9.  Detection performance comparison between SVM-based detector and other detectors[77]

      DetectorsDetection results
      (HH polarization, PF = 0.001)
      SCR = –2 dBSCR=17 dB
      SVM-based detector7699
      Tri-feature-based detector5799
      Fractal-based detector1879

      Compared with the tri-feature-based detector and fractal-based detector, the SVM-based detection method has greatly improved detection performance and can break through the limitation of the convex hull learning algorithm to detect in feature spaces of above three dimensions.

      (3) Decision tree-based sea-surface weak target detection[78]: Zhou et al. proposed a decision tree-based detector by combining the decision tree algorithm with the radar target detection problem and realized the effective control of false alarm rate. A decision tree is a common classification algorithm; the tree model is based on the attributes or characteristics of data. Common decision tree algorithms include ID3, Classification And Regression Tree (CART), C4.5, and random forest. The decision tree has only one root node, multiple internal nodes, and multiple leaf nodes. First, all the training samples are allocated to the internal nodes; as the allocation rule, the appropriate features and thresholds to maximize the purity of the internal nodes are selected. The whole process is recursive from top to bottom.

      In Zhou et al.’s study, the Hurst exponent in the time domain, the Hurst exponent in the frequency domain, and the peak-to-average ratio in the frequency domain were extracted from sea clutter. For each feature vector, the target feature vector was labeled “1” and the clutter vector was labeled “0”. A decision tree was constructed by the famous CART algorithm. First, all the feature vectors were at a root node, and the child nodes were generated by selecting the features and thresholds with the smallest Gini index (the smaller the Gini index, the higher the purity). This was repeated until the resulting child node was no longer separable, so that it became the leaf node. Finally, the generated leaf nodes could divide the feature space into two parts. The ratio of the correct target samples to all target samples was the detection probability. Fig. 32 is a flowchart of the algorithm. Tab. 10 compares the detection performances of the decision tree-based detector and the other detectors.

      Table 10.  Detection performance comparisons between the decision tree-based detector and the other detectors[78]

      DetectorDetection results
      0 dB5 dB10 dB15 dB
      Decision tree-based detector0.760.840.980.99
      Tri-feature-based detector0.580.650.820.95
      Fractal-based detector0.210.320.480.68

      Figure 32.  Flowchart of the decision tree-based detector[78]

      The decision tree-based detection method combines detection and classification, which improves the detection performance and can realize detection in high-dimensional feature spaces as well as the SVM-based method.

      The feature detection method based on machine learning algorithms can greatly improve the detection performance because the convex hull learning algorithm can only be used in the feature spaces of three dimensions or less. In contrast, machine learning algorithms can break through the limitation of high-dimensional feature space, which means more features can be used in feature-based detection to further improve the detection performance.

    • In the detection of SFTs in the sea clutter background, to obtain enough accumulated gain of target returns and improve the detection performance, the observation time usually needs to reach the second level, and in the case of long-time observation, the time series of sea clutter is non-stationary and the texture changes with time; therefore, the sea clutter time series is modeled as a compound Gaussian model with time-varying texture. Moreover, the return of SFTs shows amplitude fluctuation and Doppler modulation on the second time scale, so the target return is modeled as unknown nonlinear FM signals with amplitude fluctuation[120-122]. Therefore, the detection of sea-surface SFTs is equivalent to the problem of finding unknown nonlinear FM signals in the compound Gaussian clutter with time-varying texture.

      Because Time-Frequency Distribution (TFD) realizes the short-time coherent accumulation of unknown FM signals, it is often used to detect unknown FM signals in white noise[120-125]. When the TFD is properly designed, the energy of the nonlinear FM signal can be completely concentrated on the instantaneous frequency curve. Because the Normalized TF Distribution (NTFD) of received radar time series can enhance the TF characteristics of clutter returns, and the target return can be approximated by piecewise linear FM signals, NTFD can be computed by the SPWVD[125,126]. Sea clutter and target returns show different NTFD characteristics. Three TF features can be extracted from these differences: the ridge integration of NTFD, the number of connected regions, and the maximum size of connected regions in a binary image composed of NTFD bright pixels[75].

      Similar to the detection steps of the tri-feature-based detector, the TF-tri-feature-based detector is also divided into the training part and the detection part. In the training part, the feature vectors are extracted from a large number of pure clutter data. The single classifier is trained by the fast convex hull learning algorithm, and the mean vector and the standard deviation vector are provided for the detection part. In the detection part, the feature vectors of the received time series of the CUT and the reference cells are calculated, and the mean vector and the standard deviation vector obtained from the training part are used for normalization. The calculation of the feature vector of the CUT helps determine whether it falls into the decision region, and the decision result is obtained. The performance of the TF-tri-feature-based detector is better than that of the tri-feature-based detector, but the detection probability on some datasets is slightly decreased. This shows that feature extraction is important for the performance of the joint multi-feature-based detector.

    • The methods based on time-domain, frequency-domain, and TF-domain only use data from one of the four polarization channels: HH, HV, VH, and VV, but with the popularization of the full-polarization radar and the renewal of radar equipment, the method of full polarization is increasingly mature in the field of image processing, so it is possible to process radar data with full polarization. In Ref. [76], researchers attempted to use the full-polarization method to deal with the detection of SFTs on sea surface.

      Each set of data received by a full-polarization radar contains four kinds of data in HH, HV, VH, and VV channels. Usually, the polarimetric scattering matrix is used to characterize the received data. Based on the scattering matrix, we can extract the polarization coherent matrix or the polarization covariance matrix. Polarization features are typically extracted by decomposing the scattering matrix, the coherent matrix, or the covariance matrix. For the detection of SFTs on sea surface, surface scattering, dihedral angle scattering, and volume scattering may occur when the radar beam illuminates the calm sea surface, waves, or objects. Surface scattering occurs when the beam illuminates the calm sea surface or produce a specular reflection on a vertical wave surface or the target. Dihedral scattering may occur when the beam illuminates the dihedral angle of the wave or the angle between the target and the sea surface, and when the beam illuminates the broken wave (white cap) or the complex structure of the target, volume scattering will occur. The presence or absence of targets in the radar beam area will affect the scattering type and the scattering energy; thus, the correlation polarization features can be extracted to detect SFTs.

      Based on the theory of polarimetric target decomposition Ref. [76], according to the detection scene of SFTs on sea surface, the three-component Freeman–Durden decomposition method[127] has been used to model three scattering mechanisms, and the energy of the scattering mechanisms was determined. The relative ratio of the energy between the CUT and the reference cells was selected to extract three polarization features: the relative surface scattering power, relative dihedral scattering power, and relative volume scattering power. Using these three features, a convex hull learning algorithm was used to detect the SFT on the sea surface. Fig. 33 compares the detection performances of the polarization feature-based detector and the original tri-feature-based detector.

      Figure 33.  Detection probabilities of polarization feature-based detector and tri-detector at HH, VV, HV, and VH polarizations for 10 datasets[76]

      The polarization feature-based detector is robust and outperforms the tri-feature-based detector. Because the detector uses radar data in four polarization channels to extract separable clutter and target features, the detection performance is better than that of the detector based on a single polarization mode. When full-polarization information is used to detect targets, the information received by radar can be fully used, and the detection performance is effective; thus, further research should be done on feature extraction in full-polarization radar.

    • The detection of SFTs in the sea clutter background is a difficult problem. With the stealth and miniaturization of sea-surface targets, it is important to improve the detection ability of SFTs. The feature-based detection method of SFTs has gone through many development stages, from single-feature detection to three-features detection method to high-dimensional feature space detection based on machine learning algorithms. The range of feature extraction has also changed from one dimension in the time domain or frequency domain to two dimensions in the TF domain, and the feature has developed from single-polarization mode to full-polarization mode. In this paper, several feature-based detection methods of SFTs are introduced, and their detection principles are analyzed. There is still much room for the development of existing detectors. To improve the detection probability and the performance of detectors, the following aspects should be considered in the future development of feature-based detectors:

      (1) Extracting more features that can effectively distinguish clutter from target: The received radar signal contains a large amount of clutter and target information in various aspects, which can be used in the time domain, frequency domain, TF domain, and various levels to extract new features suitable for feature-based detection. The stronger the separability of clutter and target in the feature space, the more suitable the feature is for feature-based detection, and the detector has better detection performance.

      (2) Improving detection algorithm using new subjects: The convex hull learning algorithm commonly used in the design of single classifiers for outlier detection cannot deal with high-dimensional features. With the development of artificial intelligence, more and more learning algorithms can be used in feature-based detection. The fusion of subjects has become a trend. The multi-feature fusion algorithms can break through the limitation of high-dimensional feature space, and more features can be used in feature-based detection, which can further improve the detection performance.

      (3) Developing new radar systems: The feature-based detection method requires long observation time in the detection process, but the radar in practical applications often cannot meet the requirement of long observation time. It is difficult to reconcile the conflict between the long dwell time and the scanning efficiency of a conventional radar, which is why long-time observation is impossible. In 2003, the Lincoln Laboratory of MIT proposed the concept of a ubiquitous radar using multi-input/multi-output digital arrays[128], enabling full-time observation in all directions. The development of this kind of new radar system is more advantageous to the extraction of clutter and target features. The more the separability in feature space, the better the performance of the designed detector.

    • (1) Because of the particularity of military application, the application of artificial intelligence in the military field is far less extensive than that in the commercial Internet field. This difference is related to the available data in military artificial intelligence systems. Taking sea-surface target detection as an example, it is difficult to collect enough data to train the corresponding artificial intelligence algorithm for non-cooperative targets. Artificial intelligence algorithms, especially deep learning, typically require large amounts of data that must be accurately labeled and relevant to each particular problem domain. The decisions made may be far from optimal when the information about the military situation is corrupted or the available datasets are incomplete. Therefore, sea-surface target detection based on artificial intelligence with small or incomplete samples needs further research.

      (2) With the increasing complexity of machine learning models, we no longer have an intuitive understanding of how machines make decisions based on the commonly used machine learning methods. When machine learning models are used to recommend a movie or a restaurant, an explanation of the results may not be needed, but in the area of radar target detection, we not only want to know the results but also need to understand how these decisions are made. The interpretability technique of the current models is mainly to quantitatively evaluate each input feature and find the feature that most influences the result, or further analyze and inspect feature correlation. For tabular data, each column is a feature; for images, text, or other non-tabular data, they need to first be preprocessed to build a feature that is easy to understand. Interpretability is an important aspect of machine learning, and it is also an obstacle in the field of sea-surface target detection. Therefore, how to design interpretable artificial intelligence methods is an important subject to be studied.

      (3) In artificial intelligence-based methods, learning is a top priority. Human learning is not only about knowledge but also about the methods of acquiring data and information and the experience of knowledge. Machine learning models learn about data, information, and knowledge, but not methods. For radar detection, an accurate detector design must be based on a large amount of data in a variety of situations, and the learning process is time consuming. In a rapidly changing battlefield environment, especially for the high dynamic change of the sea environment, the speed of the detection criterion of the artificial intelligence–based detector heavily depends on the algorithm computing power, especially the hardware platform. Therefore, one of the most important difficulties in artificial intelligence application is the high efficiency of the algorithm design and the improvement of the hardware platform computing power.

    • This paper analyzes the active and difficult problems of sea-surface target detection, introduces the datasets commonly used to verify the feature-based detection methods, and then explains the principle of the detection method. Moreover, several feature-based detection methods of slow and SFTs on the sea surface are introduced, and their detection performances are analyzed. The detection of slow and SFTs is of great significance to the development of marine military. How to improve the performance of the intelligent detection of small targets is the focus of the current research. With the development of new subjects, the integration of radar target detection and various new subjects has become the development trend. Combining the intelligentization of new subjects with feature-based detection methods can result in more effective target detection and improve the detection performance of slow and SFTs in the sea clutter background.

参考文献 (128)

目录

    /

    返回文章
    返回