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The recognition of ships from chaff cloud jamming is challenging because they have similar dimensions and radar cross sections. In this paper, we propose a polarimetric recognition technique with sophisticated polarimetric target decomposition. Three sophisticated scattering models are integrated to constitute a seven-component model-based decomposition method so as to accurately characterize the dominant and local scattering of ships. Based on the concepts of contrast and suppression, a robust scattering contribution difference feature is designed according to the derived scattering contributions. The constructed feature vector, combined with the polarization scattering angle, is inputted into the support vector machine to fulfill the recognition process. Simulated and real polarimetric radar data are utilized to test the proposed method, and the results show that the proposed method outperforms state-of-the-art methods by achieving the highest recognition rate of over 98%. The recognition of ships from chaff cloud jamming is challenging because they have similar dimensions and radar cross sections. In this paper, we propose a polarimetric recognition technique with sophisticated polarimetric target decomposition. Three sophisticated scattering models are integrated to constitute a seven-component model-based decomposition method so as to accurately characterize the dominant and local scattering of ships. Based on the concepts of contrast and suppression, a robust scattering contribution difference feature is designed according to the derived scattering contributions. The constructed feature vector, combined with the polarization scattering angle, is inputted into the support vector machine to fulfill the recognition process. Simulated and real polarimetric radar data are utilized to test the proposed method, and the results show that the proposed method outperforms state-of-the-art methods by achieving the highest recognition rate of over 98%.
Obtaining the internal layout of an unfamiliar building before entering the building has important practical significance and research value, as it can be applied for various services, such as anti-terrorism operations and disaster relief. Low-frequency electromagnetic waves can propagate through common building materials, and then the target information behind the wall is obtained safely and stably. Therefore, using low frequency radio waves to obtain the information behind the wall has become the research focus in the field of building layout reconstruction. To reveal the development context of this field and predict the possible future development trends, this paper summarizes the domestic and foreign public literature in this field since the onset of the 21st century. The results of the relevant literature indicate that the techniques of using low-frequency electromagnetic waves to reconstruct building layout currently include three types: through-the-wall radar imaging technology based on reflected wave measurement, radio-frequency tomography technology based on transmitted wave measurement, and wall position estimation technology based on multipath signals. These three technologies have achieved several practical research results. This article clarifies the development history of the main content covered by these technologies, which mainly includes the principle of through-the-wall radar imaging of stationary targets behind the wall, the observation mode of building internal structure based on through-the-wall radar, the reconstruction technology of building internal structure on the basis of through-the-wall radar imaging, the inversion technology of building internal structure on the basis of radio-frequency tomography, and the wall position estimation technology based on multipath signals. We also discuss the development trend of this field. In the past two decades, the development history of building layout penetrating imaging using low-frequency radio waves shows a change from the traditional airborne and vehicle-mounted building-layout-reconstruction platforms to new platforms such as microrobots and unmanned aerial vehicles. The corresponding reconstruction method has been developed from the traditional radar imaging technology to a variety of new methods, including image enhancement and sparse reconstruction. The results indicate that the building-layout-reconstruction technology is developing in the direction of systematization, diversification, and intelligence. Obtaining the internal layout of an unfamiliar building before entering the building has important practical significance and research value, as it can be applied for various services, such as anti-terrorism operations and disaster relief. Low-frequency electromagnetic waves can propagate through common building materials, and then the target information behind the wall is obtained safely and stably. Therefore, using low frequency radio waves to obtain the information behind the wall has become the research focus in the field of building layout reconstruction. To reveal the development context of this field and predict the possible future development trends, this paper summarizes the domestic and foreign public literature in this field since the onset of the 21st century. The results of the relevant literature indicate that the techniques of using low-frequency electromagnetic waves to reconstruct building layout currently include three types: through-the-wall radar imaging technology based on reflected wave measurement, radio-frequency tomography technology based on transmitted wave measurement, and wall position estimation technology based on multipath signals. These three technologies have achieved several practical research results. This article clarifies the development history of the main content covered by these technologies, which mainly includes the principle of through-the-wall radar imaging of stationary targets behind the wall, the observation mode of building internal structure based on through-the-wall radar, the reconstruction technology of building internal structure on the basis of through-the-wall radar imaging, the inversion technology of building internal structure on the basis of radio-frequency tomography, and the wall position estimation technology based on multipath signals. We also discuss the development trend of this field. In the past two decades, the development history of building layout penetrating imaging using low-frequency radio waves shows a change from the traditional airborne and vehicle-mounted building-layout-reconstruction platforms to new platforms such as microrobots and unmanned aerial vehicles. The corresponding reconstruction method has been developed from the traditional radar imaging technology to a variety of new methods, including image enhancement and sparse reconstruction. The results indicate that the building-layout-reconstruction technology is developing in the direction of systematization, diversification, and intelligence.
The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS. The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
Full-Polarimetric Ground Penetrating Radar (FP-GPR), compared to traditional single-polarimetric GPR, can obtain more comprehensive polarization data (such as VV, HH, and VH) for the same target. To ensure a more comprehensive targets’ image identification, data fusion technology is applied to FP-GPR so as to combine the polarization information of three different polarization modes. However, weighted average fusion is usually employed in FP-GPR data fusion, since it masks the advantages of full polarization and is unable to simultaneously adapt to different target scattering mechanisms. Based on Principal Component Analysis (PCA), Laplacian Pyramid (LP), and multi-scale Wavelet Transform (WT), this research proposes three FP-GPR data fusion methods. To check the reliability of several data fusion methods, we obtained FP-GPR data representing three different target scattering mechanisms in the laboratory and, then, compared the weighted average fusion method with the other three methods using instantaneous amplitude and gradient. The result shows that the three methods were better than the weighted average fusion and that they can be adapted to different target scattering mechanisms. However, PCA was used to fuse the unknown target scattering mechanisms. Finally, PCA fusion is applied to actual ice fracture data imaging, as it produces a better fusion effect than that of weighted average fusion. Full-Polarimetric Ground Penetrating Radar (FP-GPR), compared to traditional single-polarimetric GPR, can obtain more comprehensive polarization data (such as VV, HH, and VH) for the same target. To ensure a more comprehensive targets’ image identification, data fusion technology is applied to FP-GPR so as to combine the polarization information of three different polarization modes. However, weighted average fusion is usually employed in FP-GPR data fusion, since it masks the advantages of full polarization and is unable to simultaneously adapt to different target scattering mechanisms. Based on Principal Component Analysis (PCA), Laplacian Pyramid (LP), and multi-scale Wavelet Transform (WT), this research proposes three FP-GPR data fusion methods. To check the reliability of several data fusion methods, we obtained FP-GPR data representing three different target scattering mechanisms in the laboratory and, then, compared the weighted average fusion method with the other three methods using instantaneous amplitude and gradient. The result shows that the three methods were better than the weighted average fusion and that they can be adapted to different target scattering mechanisms. However, PCA was used to fuse the unknown target scattering mechanisms. Finally, PCA fusion is applied to actual ice fracture data imaging, as it produces a better fusion effect than that of weighted average fusion.
False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images. False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.
With the emergence of stealth and jamming technology, traditional radar systems are facing great challenges in terms of innovation, number, and energy. It is necessary to develop novel detection systems, explore the initiative of cooperative detection, and utilize the dimensions of information to adapt to new air defense operations in the future. In this study, a new radar system, communicational radar, is proposed. The radar detection ability under the conditions of long-range and strong confrontation can be significantly improved by embedding synchronization information such as the transmitter position, the antenna direction, and the launch time of the emission into the waveform; the embedded information can then be extracted for target detection, reorganization, interference suppression, and multi-target identification. The proposed system is illustrated from the aspects of architecture, detection principle, and performance analysis. With the emergence of stealth and jamming technology, traditional radar systems are facing great challenges in terms of innovation, number, and energy. It is necessary to develop novel detection systems, explore the initiative of cooperative detection, and utilize the dimensions of information to adapt to new air defense operations in the future. In this study, a new radar system, communicational radar, is proposed. The radar detection ability under the conditions of long-range and strong confrontation can be significantly improved by embedding synchronization information such as the transmitter position, the antenna direction, and the launch time of the emission into the waveform; the embedded information can then be extracted for target detection, reorganization, interference suppression, and multi-target identification. The proposed system is illustrated from the aspects of architecture, detection principle, and performance analysis.
Multi-Target Tracking (MTT) is a difficult task in radar data processing. When compared to tracking in various fields or scenario, Maritime MTT (MMTT) is a challenging one and also a daunting task. On one hand, low signal-to-clutter ratio in the highly complex marine environment limits the detection performance for small targets at sea, and the plots obtained by the detector contain missing detections and a large number of false alarms, which make MTT much more difficult. On the other hand, when marine targets are moving in the form of multiple groups, or when the high resolution radar is used in marine detection applications, the measurements of the target pave the way to show efficiently the distribution characteristics of occupying multiple cells. In this case, using of conventional MTT methods is not ideal as their performance is not effective as desired. Currently, the number of papers on MMTT at home and abroad is very limited, and most of them only focus on a single target. This paper summarizes the use of MMTT algorithms based on four methods: conventional MTT method, amplitude aided MTT method, multi-target track-before-detect method, and multiple extended target-tracking method. In addition, this paper also considers and analyzes the future perspective of MMTT comprehensively. Multi-Target Tracking (MTT) is a difficult task in radar data processing. When compared to tracking in various fields or scenario, Maritime MTT (MMTT) is a challenging one and also a daunting task. On one hand, low signal-to-clutter ratio in the highly complex marine environment limits the detection performance for small targets at sea, and the plots obtained by the detector contain missing detections and a large number of false alarms, which make MTT much more difficult. On the other hand, when marine targets are moving in the form of multiple groups, or when the high resolution radar is used in marine detection applications, the measurements of the target pave the way to show efficiently the distribution characteristics of occupying multiple cells. In this case, using of conventional MTT methods is not ideal as their performance is not effective as desired. Currently, the number of papers on MMTT at home and abroad is very limited, and most of them only focus on a single target. This paper summarizes the use of MMTT algorithms based on four methods: conventional MTT method, amplitude aided MTT method, multi-target track-before-detect method, and multiple extended target-tracking method. In addition, this paper also considers and analyzes the future perspective of MMTT comprehensively.
Traditional Synthetic Aperture Radar (SAR) imaging is the projection of a real three-dimensional scene onto a two-dimensional domain of azimuth and slant range, which results in the loss of the high-dimensional information. With the advancement of SAR system and its processing technology, tomographic SAR systems obtain multiple data along the height direction to construct the high-dimensional synthetic aperture, and use array signal processing methods to achieve high-resolution three-dimensional images. It can reconstruct the observation scene and extract vertical structure information of the ground target, which is very important for vegetation monitoring, snow and ice detecting, and urban modeling. This paper analyzed the key steps of three-dimensional imaging, such as image registration, flat-earth phase removal, phase compensation, and the three-dimensional focusing, as well as the current research status of each step based on the observation mechanism of tomographic SAR system. This paper particularly focuses on using tomographic SAR on the application of vegetation, glacier, snow, and urban information. The most relevant experimental results in the past two decades were introduced. Further, the application potential and existing problems related to the vegetation height with canopy structure, glacier thickness with internal structure, snow thickness with stratification, and urban three-dimensional reconstruction with deformation monitoring under different platforms are discussed. Finally, the prospects of TomoSAR in the primary applications field are presented. Traditional Synthetic Aperture Radar (SAR) imaging is the projection of a real three-dimensional scene onto a two-dimensional domain of azimuth and slant range, which results in the loss of the high-dimensional information. With the advancement of SAR system and its processing technology, tomographic SAR systems obtain multiple data along the height direction to construct the high-dimensional synthetic aperture, and use array signal processing methods to achieve high-resolution three-dimensional images. It can reconstruct the observation scene and extract vertical structure information of the ground target, which is very important for vegetation monitoring, snow and ice detecting, and urban modeling. This paper analyzed the key steps of three-dimensional imaging, such as image registration, flat-earth phase removal, phase compensation, and the three-dimensional focusing, as well as the current research status of each step based on the observation mechanism of tomographic SAR system. This paper particularly focuses on using tomographic SAR on the application of vegetation, glacier, snow, and urban information. The most relevant experimental results in the past two decades were introduced. Further, the application potential and existing problems related to the vegetation height with canopy structure, glacier thickness with internal structure, snow thickness with stratification, and urban three-dimensional reconstruction with deformation monitoring under different platforms are discussed. Finally, the prospects of TomoSAR in the primary applications field are presented.
Two-Dimensional (2-D) autofocus is an important guarantee for high-resolution imaging of airborne Synthetic Aperture Radar (SAR) under high maneuvering conditions. The existing 2-D autofocus approaches for bistatic SAR blindly estimate the phase error and do not fully utilize the prior knowledge on phase structure. In this paper, a new interpretation of the Polar Format Algorithm (PFA) for general bistatic SAR imaging is presented. From the viewpoint of Residual Cell Migration (RCM), PFA is converted into 2-D decoupling. By utilizing this new formulation, we analyze the effect of range and azimuth resampling on the residual 2-D phase error and reveal the inherent structure characteristics of the residual 2-D phase error in the wavenumber domain. The 2-D phase error estimation can reduce to one dimensional azimuth phase error estimation. Based on this prior knowledge, a structure-aided 2-D autofocus approach is proposed. Meanwhile, the information of all the data is fully excavated by averaging sub-band data when the azimuth phase error is being estimated. Compared with the existing algorithms, both the parameter estimation precision and computational efficiency are significantly improved. Experimental results clearly demonstrate the correctness of the theoretical analysis and the effectiveness of the proposed method. Two-Dimensional (2-D) autofocus is an important guarantee for high-resolution imaging of airborne Synthetic Aperture Radar (SAR) under high maneuvering conditions. The existing 2-D autofocus approaches for bistatic SAR blindly estimate the phase error and do not fully utilize the prior knowledge on phase structure. In this paper, a new interpretation of the Polar Format Algorithm (PFA) for general bistatic SAR imaging is presented. From the viewpoint of Residual Cell Migration (RCM), PFA is converted into 2-D decoupling. By utilizing this new formulation, we analyze the effect of range and azimuth resampling on the residual 2-D phase error and reveal the inherent structure characteristics of the residual 2-D phase error in the wavenumber domain. The 2-D phase error estimation can reduce to one dimensional azimuth phase error estimation. Based on this prior knowledge, a structure-aided 2-D autofocus approach is proposed. Meanwhile, the information of all the data is fully excavated by averaging sub-band data when the azimuth phase error is being estimated. Compared with the existing algorithms, both the parameter estimation precision and computational efficiency are significantly improved. Experimental results clearly demonstrate the correctness of the theoretical analysis and the effectiveness of the proposed method.
Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies. Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies.
Aircraft wake are a couple of counter-rotating vortices generated by a flying aircraft, which can be very hazardous to a follower aircraft. The detection of it is regarded as a key issue for airport capacity improvement and air traffic safety management. To this end, we constructed a Lidar detection based aircraft wake vortex parameter-retrieval system, which can be used to retrieve the vortex-core positions and circulations from detected data. Furthermore, dynamics, scattering and Lidar echo simulation modules were built to validate the parameter-retrieval algorithms. Results show that the proposed system performs well and runs steadily, which can serve as a good tool for aircraft wake vortex characterization, prediction, and is very helpful to establish dynamic wake separation in air traffic management. Aircraft wake are a couple of counter-rotating vortices generated by a flying aircraft, which can be very hazardous to a follower aircraft. The detection of it is regarded as a key issue for airport capacity improvement and air traffic safety management. To this end, we constructed a Lidar detection based aircraft wake vortex parameter-retrieval system, which can be used to retrieve the vortex-core positions and circulations from detected data. Furthermore, dynamics, scattering and Lidar echo simulation modules were built to validate the parameter-retrieval algorithms. Results show that the proposed system performs well and runs steadily, which can serve as a good tool for aircraft wake vortex characterization, prediction, and is very helpful to establish dynamic wake separation in air traffic management.
Owing to their strong anti-stealth performance, good concealment and strong survivability, passive radar systems have a wide range of applications in both military and civilian fields. We propose a method of target detection for passive radar systems which is based on the characteristics of these systems and the track-before-detect concept. This method accumulates information to effectively detect weak targets with low signal-to-noise ratios and meet real-time requirements. First, we discretize the state space, then perform recursive Bayesian filtering to transfer and accumulate target-state information between multiple frames. Lastly, the information entropy is used to determine whether the target exists, thereby avoiding reliance on a prior assumption about the transition probability model between the existence and the absence of the target. This method is simple to implement and has low computational complexity and high parallelism. The experimental results indicate that the proposed method has a short running time and strong real-time performance, as well as good detection performance and robustness. Owing to their strong anti-stealth performance, good concealment and strong survivability, passive radar systems have a wide range of applications in both military and civilian fields. We propose a method of target detection for passive radar systems which is based on the characteristics of these systems and the track-before-detect concept. This method accumulates information to effectively detect weak targets with low signal-to-noise ratios and meet real-time requirements. First, we discretize the state space, then perform recursive Bayesian filtering to transfer and accumulate target-state information between multiple frames. Lastly, the information entropy is used to determine whether the target exists, thereby avoiding reliance on a prior assumption about the transition probability model between the existence and the absence of the target. This method is simple to implement and has low computational complexity and high parallelism. The experimental results indicate that the proposed method has a short running time and strong real-time performance, as well as good detection performance and robustness.
Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD. Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification. Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment. The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment.
This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar. This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar.