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With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed. With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed.
As an important tool for acquiring remote sensing information, Synthetic Aperture Radar (SAR) has various modes, including high-resolution wide-swath, multi-angle information acquisition, high temporal observation, and three-dimensional topographic mapping. For any spaceborne SAR system, obtaining high-quality images is a prerequisite for improving the performance of SAR applications. In this paper, we analyze the factors affecting spaceborne SAR imaging and image quality with respect to orbit, platform, payload, and signal processing. We describe high-precision data acquisition techniques, including amplitude-phase compensation, the dynamic adjustment of the central electronic equipment, and antenna pattern estimation. We then present imaging compensation methods based on the improved motion model and tropospheric delay correction, which can achieve resolutions better than 0.3 m. Lastly, we summarize and compare SAR image processing techniques such as speckle noise suppression, azimuth ambiguity suppression, and sidelobe suppression, whereby the equivalent number of looks can be increased to more than 25 and the azimuth ambiguity and sidelobes can both be suppressed by 20 dB. As an important tool for acquiring remote sensing information, Synthetic Aperture Radar (SAR) has various modes, including high-resolution wide-swath, multi-angle information acquisition, high temporal observation, and three-dimensional topographic mapping. For any spaceborne SAR system, obtaining high-quality images is a prerequisite for improving the performance of SAR applications. In this paper, we analyze the factors affecting spaceborne SAR imaging and image quality with respect to orbit, platform, payload, and signal processing. We describe high-precision data acquisition techniques, including amplitude-phase compensation, the dynamic adjustment of the central electronic equipment, and antenna pattern estimation. We then present imaging compensation methods based on the improved motion model and tropospheric delay correction, which can achieve resolutions better than 0.3 m. Lastly, we summarize and compare SAR image processing techniques such as speckle noise suppression, azimuth ambiguity suppression, and sidelobe suppression, whereby the equivalent number of looks can be increased to more than 25 and the azimuth ambiguity and sidelobes can both be suppressed by 20 dB.
The development of multimode high-resolution Synthetic Aperture Radar (SAR) poses new challenges to information perception and feature acquisition of the space, ground, and sea environment targets. The intersection of spatial remote sensing big data and artificial intelligence information technology is a new scientific research domain and major application area in Automatic Target Recognition (ATR). We emphasize that research on artificial intelligence information technology needs to be conducted under the physical background of the interaction between electromagnetic waves and targets, i.e., physical intelligence, to develop microwave vision of information perception on the electromagnetic spectrum that cannot be recognized by human eyes. This study is based on a report presented to the editorial board of Journal of Radars and at the Fifth Young Scientists Forum on August 14, 2019. The development of multimode high-resolution Synthetic Aperture Radar (SAR) poses new challenges to information perception and feature acquisition of the space, ground, and sea environment targets. The intersection of spatial remote sensing big data and artificial intelligence information technology is a new scientific research domain and major application area in Automatic Target Recognition (ATR). We emphasize that research on artificial intelligence information technology needs to be conducted under the physical background of the interaction between electromagnetic waves and targets, i.e., physical intelligence, to develop microwave vision of information perception on the electromagnetic spectrum that cannot be recognized by human eyes. This study is based on a report presented to the editorial board of Journal of Radars and at the Fifth Young Scientists Forum on August 14, 2019.
In complex electromagnetic environment, the clutter covariance matrix is required to estimate in the on-line manner, so as to adjust the filter weight adaptively to suppress the clutter effectively, which is helpful to target estimation, detection, location and tracking. In this paper, the non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, and the signal of interest is located in the subspace. To this end, a knowledge-aided hierarchical Bayesian model is proposed, and the approximated posterior distribution of the clutter covariance matrix is obtained by exploiting the Variational Bayesian Inference methods. The target detection performance can be enhanced by the clutter suppress filter designed by the posterior mean of the clutter covariance matrix. The computer simulation and real radar data validate that the proposed method can suppress the clutter meanwhile the detection performance can be improved. In complex electromagnetic environment, the clutter covariance matrix is required to estimate in the on-line manner, so as to adjust the filter weight adaptively to suppress the clutter effectively, which is helpful to target estimation, detection, location and tracking. In this paper, the non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, and the signal of interest is located in the subspace. To this end, a knowledge-aided hierarchical Bayesian model is proposed, and the approximated posterior distribution of the clutter covariance matrix is obtained by exploiting the Variational Bayesian Inference methods. The target detection performance can be enhanced by the clutter suppress filter designed by the posterior mean of the clutter covariance matrix. The computer simulation and real radar data validate that the proposed method can suppress the clutter meanwhile the detection performance can be improved.
Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also improving the ground detection performance of the SAR system. However, co-prime-sampling in azimuth causes the echo signal to exhibit azimuthal under sampling and non-uniform sampling characteristics, which means the traditional SAR image-processing method can not effectively image co-prime-sampled SAR. In this paper, an imaging method based on Two-Dimensional (2D) sparse-signal reconstruction is proposed for co-prime-sampling space-borne SAR. Using this method, after range-pulse compression, the 2D observed signal is intercepted and a corresponding sparse dictionary consisting of 2D atoms is constructed according to the Doppler parameters of each range gate. Then, azimuth-focus processing is completed by the improved 2D-signal sparsity adaptive matching pursuit algorithm. The proposed method not only compensates for the 2D coupling between the range and azimuth, but also eliminates the influence of space-varying imaging parameters on sparse reconstruction to achieve accurate reconstruction of the entire scene. The simulation results of the point targets and distribution targets verify that the proposed method can effectively reconstruct sparse scenes at a rate much lower than the Nyquist sampling rate. Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also improving the ground detection performance of the SAR system. However, co-prime-sampling in azimuth causes the echo signal to exhibit azimuthal under sampling and non-uniform sampling characteristics, which means the traditional SAR image-processing method can not effectively image co-prime-sampled SAR. In this paper, an imaging method based on Two-Dimensional (2D) sparse-signal reconstruction is proposed for co-prime-sampling space-borne SAR. Using this method, after range-pulse compression, the 2D observed signal is intercepted and a corresponding sparse dictionary consisting of 2D atoms is constructed according to the Doppler parameters of each range gate. Then, azimuth-focus processing is completed by the improved 2D-signal sparsity adaptive matching pursuit algorithm. The proposed method not only compensates for the 2D coupling between the range and azimuth, but also eliminates the influence of space-varying imaging parameters on sparse reconstruction to achieve accurate reconstruction of the entire scene. The simulation results of the point targets and distribution targets verify that the proposed method can effectively reconstruct sparse scenes at a rate much lower than the Nyquist sampling rate.
Spaceborne Synthetic Aperture Radar (SAR) is a type of microwave imaging radar with 2D high resolution. This technological device achieves range high resolution by transmitting wideband signals and azimuth high resolution through the synthetic aperture approach. With the increasing demand for high-resolution imaging, the resolution of spaceborne SAR has moved toward the decimeter level. On the one hand, limited by the present hardware technology, achieving wideband signal transmission through stepped-frequency technology is necessary. In this case, we need to study high-precision bandwidth synthesis technology. The influence of slant range error and amplitude and phase error between sub-bands should be considered. On the other hand, due to limited beamwidth, the system needs to work in sliding spot mode to achieve a long synthetic aperture. In this case, we need to study the problem of imaging parameter variance caused by curved orbit, " stop–go” error, and the influence of ionospheric and tropospheric transmission errors on imaging. To solve these problems, this paper introduces the principle of stepped-frequency signal design and bandwidth synthesis technology in detail. A time-domain algorithm and non-ideal factor compensation method are proposed for spaceborne high-resolution stepped-frequency SAR imaging. Finally, simulation verification and performance analysis of the imaging algorithm are conducted. Spaceborne Synthetic Aperture Radar (SAR) is a type of microwave imaging radar with 2D high resolution. This technological device achieves range high resolution by transmitting wideband signals and azimuth high resolution through the synthetic aperture approach. With the increasing demand for high-resolution imaging, the resolution of spaceborne SAR has moved toward the decimeter level. On the one hand, limited by the present hardware technology, achieving wideband signal transmission through stepped-frequency technology is necessary. In this case, we need to study high-precision bandwidth synthesis technology. The influence of slant range error and amplitude and phase error between sub-bands should be considered. On the other hand, due to limited beamwidth, the system needs to work in sliding spot mode to achieve a long synthetic aperture. In this case, we need to study the problem of imaging parameter variance caused by curved orbit, " stop–go” error, and the influence of ionospheric and tropospheric transmission errors on imaging. To solve these problems, this paper introduces the principle of stepped-frequency signal design and bandwidth synthesis technology in detail. A time-domain algorithm and non-ideal factor compensation method are proposed for spaceborne high-resolution stepped-frequency SAR imaging. Finally, simulation verification and performance analysis of the imaging algorithm are conducted.
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Special Topic Papers: Advanced Technologies on Radar Detection and Imaging
Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future. Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future.
Array signal processing is an essential tool in broad radar applications. The coprime array has recently been proposed to overcome the bottleneck caused by the Nyquist spatial sampling rate. The coprime array, whose sparse structure and undersampling feature drastically decrease necessary computational and hardware cost, provides a theoretical foundation and technical basis for the increasing demands of its practical applications. Considering its superior performance in degrees-of-freedom, spatial resolution, and computational complexity, research on coprime array signal processing has attracted much attention. This paper reviews recent research progress on coprime array signal processing, which has focused on both the Direction-of-Arrival (DOA) estimation and adaptive beamforming. From the perspective of coprime array DOA estimation, this paper summarizes two typical approaches, namely the coprime subarray decomposition-based approach and the virtual array signal processing-based approach. Moreover, recent work on low-complexity and super-resolution DOA estimation via compressive sensing and gridless techniques is also introduced. From the perspective of coprime array adaptive beamforming, the differences and relationships between DOA estimation and beamforming in the framework of coprime array signal processing are discussed, and an efficient, robust, and adaptive beamformer design tailored for the coprime array is introduced. Advantages and the future directions of coprime array signal processing are discussed, along with the theoretical basis and a technical reference for practical radar applications. Array signal processing is an essential tool in broad radar applications. The coprime array has recently been proposed to overcome the bottleneck caused by the Nyquist spatial sampling rate. The coprime array, whose sparse structure and undersampling feature drastically decrease necessary computational and hardware cost, provides a theoretical foundation and technical basis for the increasing demands of its practical applications. Considering its superior performance in degrees-of-freedom, spatial resolution, and computational complexity, research on coprime array signal processing has attracted much attention. This paper reviews recent research progress on coprime array signal processing, which has focused on both the Direction-of-Arrival (DOA) estimation and adaptive beamforming. From the perspective of coprime array DOA estimation, this paper summarizes two typical approaches, namely the coprime subarray decomposition-based approach and the virtual array signal processing-based approach. Moreover, recent work on low-complexity and super-resolution DOA estimation via compressive sensing and gridless techniques is also introduced. From the perspective of coprime array adaptive beamforming, the differences and relationships between DOA estimation and beamforming in the framework of coprime array signal processing are discussed, and an efficient, robust, and adaptive beamformer design tailored for the coprime array is introduced. Advantages and the future directions of coprime array signal processing are discussed, along with the theoretical basis and a technical reference for practical radar applications.
Active mm-wave linear-array 3D imaging system has become one of the active research areas in the field of imaging for human security. In this paper, the operating mode, signal model, and imaging algorithm are introduced. Deep learning algorithms, including the Convolutional Neural Network (CNN) with heat map and You Only Look Once (YOLO) network, were used for the object detection of human security image. The results show that the method based on heat map and YOLO can both detect foreign objects. We find that the CNN with heat map has a simple network construction and can be easily trained, but the detection process needs to traverse the whole image, which is relatively time-consuming, and the size of the detection region cannot adapt to the objects. On the contrary, though with a relatively complex construction, YOLO network has advantages in terms of detection efficiency and accuracy. Furthermore, the size of the detection region can adapt to the objects, which is more suitable for the human security imaging application. Active mm-wave linear-array 3D imaging system has become one of the active research areas in the field of imaging for human security. In this paper, the operating mode, signal model, and imaging algorithm are introduced. Deep learning algorithms, including the Convolutional Neural Network (CNN) with heat map and You Only Look Once (YOLO) network, were used for the object detection of human security image. The results show that the method based on heat map and YOLO can both detect foreign objects. We find that the CNN with heat map has a simple network construction and can be easily trained, but the detection process needs to traverse the whole image, which is relatively time-consuming, and the size of the detection region cannot adapt to the objects. On the contrary, though with a relatively complex construction, YOLO network has advantages in terms of detection efficiency and accuracy. Furthermore, the size of the detection region can adapt to the objects, which is more suitable for the human security imaging application.
To address the problem of radar High-Resolution Range Profile (HRRP) target recognition, traditional methods only consider the envelope information of the sample and ignore the temporal correlation between the range cells. In this study, we propose a bidirectional self-recurrent neural network model based on an attention mechanism. The model divides the HRRP data in the time domain into two sequences, i.e., forward and backward using a sliding window, then extracts the features through two independent GRU networks, and splices the extracted features simultaneously, thus utilizing the bidirectional temporal information of HRRP. Considering that sequences at different moments have different degrees of importance to the target classification, different attention weights are assigned to the hidden layer features at each moment. Finally, the model uses the hidden features weighted summation to obtain target recognition and classification result. Experimental results show that the proposed method can effectively solve the target recognition problem of HRRP, and that the target area can still be accurately identified when the time shift occurs. To address the problem of radar High-Resolution Range Profile (HRRP) target recognition, traditional methods only consider the envelope information of the sample and ignore the temporal correlation between the range cells. In this study, we propose a bidirectional self-recurrent neural network model based on an attention mechanism. The model divides the HRRP data in the time domain into two sequences, i.e., forward and backward using a sliding window, then extracts the features through two independent GRU networks, and splices the extracted features simultaneously, thus utilizing the bidirectional temporal information of HRRP. Considering that sequences at different moments have different degrees of importance to the target classification, different attention weights are assigned to the hidden layer features at each moment. Finally, the model uses the hidden features weighted summation to obtain target recognition and classification result. Experimental results show that the proposed method can effectively solve the target recognition problem of HRRP, and that the target area can still be accurately identified when the time shift occurs.
Pest migration has the characteristics of large scale and strong suddenness, which will lead to the outbreaks of pests and diseases, the decline of grain yield, and considerable economic losses. Entomological radar is an effective means of monitoring migratory pests. However, the Radar Cross Section (RCS) of an insect target is small, whereas the echo power is weak. High detection probability will result in a high false alarm probability. In the data association step of target tracking, the association error occurs due to the influence of false measurement. By utilizing the amplitude difference between the target and noise, the amplitude information-assisted tracking algorithm can effectively improve the recognition degree toward the target and noise and improve the tracking performance. However, the RCS fluctuation model of the target is needed as prior information to calculate the amplitude likelihood ratio. Therefore, in this paper, the insect RCS fluctuating characteristics are analyzed based on Ku-band entomological radar experiment data. The results show that gamma distribution can fit well the RCS probability distribution of the insect target. On this basis, we derive the amplitude likelihood ratio of the gamma fluctuation target in Gaussian white-noise background. By analyzing the simulation results and performance under different signal-to-noise ratios, measurement noises, and fluctuation model parameters, compared with probabilistic data association filter, the RCS feature-aided tracking algorithm can effectively improve the insect target tracking accuracy. Pest migration has the characteristics of large scale and strong suddenness, which will lead to the outbreaks of pests and diseases, the decline of grain yield, and considerable economic losses. Entomological radar is an effective means of monitoring migratory pests. However, the Radar Cross Section (RCS) of an insect target is small, whereas the echo power is weak. High detection probability will result in a high false alarm probability. In the data association step of target tracking, the association error occurs due to the influence of false measurement. By utilizing the amplitude difference between the target and noise, the amplitude information-assisted tracking algorithm can effectively improve the recognition degree toward the target and noise and improve the tracking performance. However, the RCS fluctuation model of the target is needed as prior information to calculate the amplitude likelihood ratio. Therefore, in this paper, the insect RCS fluctuating characteristics are analyzed based on Ku-band entomological radar experiment data. The results show that gamma distribution can fit well the RCS probability distribution of the insect target. On this basis, we derive the amplitude likelihood ratio of the gamma fluctuation target in Gaussian white-noise background. By analyzing the simulation results and performance under different signal-to-noise ratios, measurement noises, and fluctuation model parameters, compared with probabilistic data association filter, the RCS feature-aided tracking algorithm can effectively improve the insect target tracking accuracy.
Parameter estimation of Attributed Scattering Centers (ASCs) corresponding to scattering geometries on targets plays an important role in Synthetic Aperture Radar (SAR) imaging-assisted Automatic Target Recognition (ATR). To achieve computational savings and clutter suppression, we extract the measurements of several ASCs and estimate the parameters of each ASC separately. To improve the speed of the estimation process, we propose a method for parameter estimation of ASCs based on amplitude–phase separation that considers a reasonable assumption that the amplitude- and phase-related parameters of an ASC can be estimated separately and independently. Through the proposed method, the complexity and time consumed for parameter estimation are reduced by one order of magnitude than the traditional method. The Iterative Half Thresholding (IHT) algorithm is introduced to enhance the accuracy of parameter estimation. The types and locations of scattering geometries on the target are determined using the estimated ASC parameters. Using simulated data, measured data, and MSTAR data sets, the accuracy and efficiency of parameter estimation are improved and the effectiveness of the proposed method is verified. Parameter estimation of Attributed Scattering Centers (ASCs) corresponding to scattering geometries on targets plays an important role in Synthetic Aperture Radar (SAR) imaging-assisted Automatic Target Recognition (ATR). To achieve computational savings and clutter suppression, we extract the measurements of several ASCs and estimate the parameters of each ASC separately. To improve the speed of the estimation process, we propose a method for parameter estimation of ASCs based on amplitude–phase separation that considers a reasonable assumption that the amplitude- and phase-related parameters of an ASC can be estimated separately and independently. Through the proposed method, the complexity and time consumed for parameter estimation are reduced by one order of magnitude than the traditional method. The Iterative Half Thresholding (IHT) algorithm is introduced to enhance the accuracy of parameter estimation. The types and locations of scattering geometries on the target are determined using the estimated ASC parameters. Using simulated data, measured data, and MSTAR data sets, the accuracy and efficiency of parameter estimation are improved and the effectiveness of the proposed method is verified.
In multichannel Synthetic Aperture Radar (SAR) data processing, the phase and amplitude characteristics of all channels should be well calibrated before multichannel data reconstruction; otherwise, the imaging result will be degraded and suffer from ghost targets. The yaw and pitch of the SAR platform, which change in azimuth and range, respectively, will cause phase mismatch among different channels. The currently developed attitude-aided phase mismatch calibration method does not consider the topographic relief. On the basis of the external Digital Elevation Model (DEM) and attitude information, a new phase mismatch calibration method is proposed in this study. The proposed method performs better than other methods in mountainous areas. A simulation experiment and the corresponding quantitative assessment are conducted. Then, the newly proposed method is applied to airborne multichannel SAR experimental data for further verification. In multichannel Synthetic Aperture Radar (SAR) data processing, the phase and amplitude characteristics of all channels should be well calibrated before multichannel data reconstruction; otherwise, the imaging result will be degraded and suffer from ghost targets. The yaw and pitch of the SAR platform, which change in azimuth and range, respectively, will cause phase mismatch among different channels. The currently developed attitude-aided phase mismatch calibration method does not consider the topographic relief. On the basis of the external Digital Elevation Model (DEM) and attitude information, a new phase mismatch calibration method is proposed in this study. The proposed method performs better than other methods in mountainous areas. A simulation experiment and the corresponding quantitative assessment are conducted. Then, the newly proposed method is applied to airborne multichannel SAR experimental data for further verification.
Interference Synthetic Aperture Radar based on the Global Navigation Satellite System (GNSS-InSAR) uses in-orbit navigation satellites as transmitters of opportunity and receivers are deployed near the ground. Continuous regional observation can be achieved by the constellation and repeat-pass characteristics of the navigation satellites. Continuous-time data collection is required for 1D/3D deformation retrieval of the scene, just like city, bridge, and slope. Since the navigation satellites are not strictly repeat pass and time of repeat pass is uncertain, the original data redundancy is high and interception amount is large when data are aligned, reducing the effect of data. This study focuses on the time accuracy of data acquisition in deformation retrieval of GNSS-InSAR and proposes a repeat-pass data acquisition optimization model, which combines the actual trajectory with the STK, two-line element set prediction trajectory, and sliding window trajectory of the spatial coherence coefficient. Data are aligned to determine the time interval of the adjacent navigation satellites, enabling accurate GNSS-InSAR data acquisition and ensuring effective data accumulation time under reduced original data redundancy. The measured data show the effectiveness of the proposed method. Interference Synthetic Aperture Radar based on the Global Navigation Satellite System (GNSS-InSAR) uses in-orbit navigation satellites as transmitters of opportunity and receivers are deployed near the ground. Continuous regional observation can be achieved by the constellation and repeat-pass characteristics of the navigation satellites. Continuous-time data collection is required for 1D/3D deformation retrieval of the scene, just like city, bridge, and slope. Since the navigation satellites are not strictly repeat pass and time of repeat pass is uncertain, the original data redundancy is high and interception amount is large when data are aligned, reducing the effect of data. This study focuses on the time accuracy of data acquisition in deformation retrieval of GNSS-InSAR and proposes a repeat-pass data acquisition optimization model, which combines the actual trajectory with the STK, two-line element set prediction trajectory, and sliding window trajectory of the spatial coherence coefficient. Data are aligned to determine the time interval of the adjacent navigation satellites, enabling accurate GNSS-InSAR data acquisition and ensuring effective data accumulation time under reduced original data redundancy. The measured data show the effectiveness of the proposed method.
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The vortex electromagnetic wave, which carries the Orbital Angular Momentum (OAM), reflects a new degree of freedom in addition to the traditional degrees of freedom such as intensity, phase, frequency, and polarization. Theoretically, vortex electromagnetic wave, at any frequency, has an infinite number of orthogonal modes that do not interfere with each other, and in recent years, they have shown important potential applications in the fields of radar imaging, wireless communication and so on. Therefore, they have attracted considerable attention from scholars worldwide owing to their high research value and application prospects. Here, this paper mainly introduces the recent research advances on the antenna technology of vortex electromagnetic wave, including single microstrip patch antenna, array antenna, traveling wave antenna, and metasurface antenna structure. The single microstrip patch antenna is widely used owing to its simple structure and low manufacturing cost. The traveling wave antenna can generate multi-OAM mode vortex electromagnetic waves in a wide-frequency range. The array antenna is easy to design and controllably generate high-gain OAM electromagnetic waves with different modes. The metasurface antennas do not require complex feeding networks, which has the advantage of a lower profile of the antenna. Finally, we summarize these four common vortex antennas and further look forward to their future developments. The vortex electromagnetic wave, which carries the Orbital Angular Momentum (OAM), reflects a new degree of freedom in addition to the traditional degrees of freedom such as intensity, phase, frequency, and polarization. Theoretically, vortex electromagnetic wave, at any frequency, has an infinite number of orthogonal modes that do not interfere with each other, and in recent years, they have shown important potential applications in the fields of radar imaging, wireless communication and so on. Therefore, they have attracted considerable attention from scholars worldwide owing to their high research value and application prospects. Here, this paper mainly introduces the recent research advances on the antenna technology of vortex electromagnetic wave, including single microstrip patch antenna, array antenna, traveling wave antenna, and metasurface antenna structure. The single microstrip patch antenna is widely used owing to its simple structure and low manufacturing cost. The traveling wave antenna can generate multi-OAM mode vortex electromagnetic waves in a wide-frequency range. The array antenna is easy to design and controllably generate high-gain OAM electromagnetic waves with different modes. The metasurface antennas do not require complex feeding networks, which has the advantage of a lower profile of the antenna. Finally, we summarize these four common vortex antennas and further look forward to their future developments.
To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solid-state phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology. To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solid-state phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.

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