Current Issue

2022 Vol. 11, No. 5
Radar Target Detection and Recognition
Automatic Target Recognition (ATR) is a special engineering application field which is closely related to signal and information processing, pattern recognition, artificial intelligence and other disciplines. Owing to the inherent uncertainty of ATR systems, the complexity of the recognition environment, and the increasingly adversarial nature of recognition, the development of ATR faces systematic challenges from theory to technology and applications. This paper presents the definition and connotation of ATR from an engineering perspective, briefly reviews and analyzes the developments in this field, explores the core technology system and system development model of ATR, and finally examines the future development challenges. Automatic Target Recognition (ATR) is a special engineering application field which is closely related to signal and information processing, pattern recognition, artificial intelligence and other disciplines. Owing to the inherent uncertainty of ATR systems, the complexity of the recognition environment, and the increasingly adversarial nature of recognition, the development of ATR faces systematic challenges from theory to technology and applications. This paper presents the definition and connotation of ATR from an engineering perspective, briefly reviews and analyzes the developments in this field, explores the core technology system and system development model of ATR, and finally examines the future development challenges.
The traditional coherent radar signal processing generally adopts the cascaded processing method of pulse compression and Radon-Fourier Transform (RFT) for a target moving across the range cell. However, the cascaded processing exhibits the following problems: first, during the energy integration of a high-speed target, problems including the offset of the target peak, even broadening of the main lobe, gain reduction and increases in the side lobes will occur; second, the lack of effective clutter suppression affects the detection of weak targets. Based on the multi-dimensional signal combination and clutter suppression, this paper proposes a Time-Range Focus-Before-Detect method (Adaptive-Pulse Compression Radon-Fourier Transform, A-PCRFT) in clutter background, which combines the pulse compression, RFT and adaptive clutter suppression. First, the method combines the two radar signal processing dimensions of intra-pulse time (fast time) and inter-pulse time (slow time). The two-dimensional steering vector corresponding to the high-speed target is introduced to compensate for the intra-pulse time and inter-pulse Doppler shifts; Then, the clutter covariance matrix before pulse compression is estimated based on the secondary data; Finally, the optimal filter weight vector is determined according to the clutter covariance matrix and the steering vector. This method can effectively suppress the clutter and focus the target energy simultaneously in the range-velocity space. Simulation results show that this method is superior to the cascaded method, which adopts the pulse compression and adaptive Radon–Fourier transform. The traditional coherent radar signal processing generally adopts the cascaded processing method of pulse compression and Radon-Fourier Transform (RFT) for a target moving across the range cell. However, the cascaded processing exhibits the following problems: first, during the energy integration of a high-speed target, problems including the offset of the target peak, even broadening of the main lobe, gain reduction and increases in the side lobes will occur; second, the lack of effective clutter suppression affects the detection of weak targets. Based on the multi-dimensional signal combination and clutter suppression, this paper proposes a Time-Range Focus-Before-Detect method (Adaptive-Pulse Compression Radon-Fourier Transform, A-PCRFT) in clutter background, which combines the pulse compression, RFT and adaptive clutter suppression. First, the method combines the two radar signal processing dimensions of intra-pulse time (fast time) and inter-pulse time (slow time). The two-dimensional steering vector corresponding to the high-speed target is introduced to compensate for the intra-pulse time and inter-pulse Doppler shifts; Then, the clutter covariance matrix before pulse compression is estimated based on the secondary data; Finally, the optimal filter weight vector is determined according to the clutter covariance matrix and the steering vector. This method can effectively suppress the clutter and focus the target energy simultaneously in the range-velocity space. Simulation results show that this method is superior to the cascaded method, which adopts the pulse compression and adaptive Radon–Fourier transform.
This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch. This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch.
This paper investigates the joint optimization problem of transmit resources and trajectory planning for target tracking in airborne radar networks. First, the analytical expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes is derived and adopted as the metric function to evaluate the target tracking accuracy. In addition, the analytical expression of intercept probability with the variables of the radar transmit power, dwell time, and speed and heading angle of airborne nodes is also derived and utilized as the metric function to gauge the radio frequency stealth performance of the overall system. On this basis, a joint optimization model of transmit resources and trajectory planning for target tracking in airborne radar networks is established to jointly optimize the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes. This is done to minimize the target estimation error BCRLB under the constraints of given system resources, aircraft maneuvering and intercept probability threshold, thereby improving the target tracking accuracy of airborne radar network. Subsequently, a five-step decomposition iterative algorithm incorporating the particle swarm algorithm is used to solve the underlying optimization problem. The simulation results demonstrate that the target tracking accuracy of the proposed algorithm outperforms other existing approaches. This paper investigates the joint optimization problem of transmit resources and trajectory planning for target tracking in airborne radar networks. First, the analytical expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes is derived and adopted as the metric function to evaluate the target tracking accuracy. In addition, the analytical expression of intercept probability with the variables of the radar transmit power, dwell time, and speed and heading angle of airborne nodes is also derived and utilized as the metric function to gauge the radio frequency stealth performance of the overall system. On this basis, a joint optimization model of transmit resources and trajectory planning for target tracking in airborne radar networks is established to jointly optimize the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes. This is done to minimize the target estimation error BCRLB under the constraints of given system resources, aircraft maneuvering and intercept probability threshold, thereby improving the target tracking accuracy of airborne radar network. Subsequently, a five-step decomposition iterative algorithm incorporating the particle swarm algorithm is used to solve the underlying optimization problem. The simulation results demonstrate that the target tracking accuracy of the proposed algorithm outperforms other existing approaches.
Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo. Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo.
Array Radar Technology
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity. As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
Due to the range dependence and time-varying array factor of Frequency Diverse Array (FDA) radar, it can overcome the miss of range variable in traditional phased-array factor and gain loss of Multiple-Input Multiple-Output (MIMO) radar array. In recent years, FDA radar techniques have attracted more and more attention of researches and institutions. Nevertheless, there are still many open problems to be solved in FDA radar system theory, signal processing and application implementation. In this overviewing paper, we introduced the FDA concepts, motivation and extending techniques. The latest research advances on FDA radars and their applications are comprehensively reviewed, and the typical application prospects of FDA in jamming radar and radar anti-jamming, ambiguous clutter suppression and blind velocity target detection together with localization deception are discussed. Finally, several key research problems that need to be solved in future work are pointed out. Due to the range dependence and time-varying array factor of Frequency Diverse Array (FDA) radar, it can overcome the miss of range variable in traditional phased-array factor and gain loss of Multiple-Input Multiple-Output (MIMO) radar array. In recent years, FDA radar techniques have attracted more and more attention of researches and institutions. Nevertheless, there are still many open problems to be solved in FDA radar system theory, signal processing and application implementation. In this overviewing paper, we introduced the FDA concepts, motivation and extending techniques. The latest research advances on FDA radars and their applications are comprehensively reviewed, and the typical application prospects of FDA in jamming radar and radar anti-jamming, ambiguous clutter suppression and blind velocity target detection together with localization deception are discussed. Finally, several key research problems that need to be solved in future work are pointed out.
A Frequency Diverse Array (FDA), developed innovatively based on phased array radar, can obtain an angle-range-time-dependent multidimensional transmit beampattern by modulating frequencies across different transmit antenna elements, which considerably increases the beam control ability and signal processing dimension. After joint transmit-receive processing, an FDA can be applied to various areas, such as multidimensional parameter joint estimation, mainlobe deceptive jammer suppression, ambiguous clutter suppression, and high-resolution and wide-swath imaging. This study investigates the waveform design and signal processing method of a multifunctional integrated system based on an FDA from the system level, with emphasis on new signal processing methods for integrated detection and estimation, integrated ambiguity resolution and jammer suppression, as well as integrated Synthetic Aperture Radar (SAR) imaging and moving target detection. Moreover, the application prospects of FDA multifunctional integrated systems are provided. A Frequency Diverse Array (FDA), developed innovatively based on phased array radar, can obtain an angle-range-time-dependent multidimensional transmit beampattern by modulating frequencies across different transmit antenna elements, which considerably increases the beam control ability and signal processing dimension. After joint transmit-receive processing, an FDA can be applied to various areas, such as multidimensional parameter joint estimation, mainlobe deceptive jammer suppression, ambiguous clutter suppression, and high-resolution and wide-swath imaging. This study investigates the waveform design and signal processing method of a multifunctional integrated system based on an FDA from the system level, with emphasis on new signal processing methods for integrated detection and estimation, integrated ambiguity resolution and jammer suppression, as well as integrated Synthetic Aperture Radar (SAR) imaging and moving target detection. Moreover, the application prospects of FDA multifunctional integrated systems are provided.
The clutter of space-based early warning radar exhibits tight coupling in the azimuth-elevation-Doppler domain due to the high speed of satellites and the Earth’s rotation. As a result, conventional Space-Time Adaptive Processing (STAP) suffers significant performance degradation when detecting slow moving targets. The azimuth-elevation-Doppler three-dimensional STAP method provides the ability to decouple clutter and thus can achieve sub-optimal performance for clutter suppression. However, in contrast to the situation in non-sidelooking airborne early warning radar, this method requires large system degrees of freedom when applied to space-based early warning radar. Therefore, in practice, both the computational load and the sample requirement are too large to meet. In this study, the space-time signal model of the planar array for space-based early warning radar is first constructed. Then, the tight coupling characteristic of clutter in the azimuth-elevation-Doppler domain is analyzed in detail. On this basis, a novel three-dimensional STAP method with reduced degrees of freedom with factored structure is proposed. The sidelobe clutter is first suppressed via amplitude taper in azimuth, and the mainlobe clutter responding to each ambiguous range is further canceled by adaptive processing in the elevation-Doppler domain. The simulation results show that the proposed method can achieve sub-optimal performance under low computational load and limited sample conditions. Therefore, the proposed method is suitable for practical application in space-based early warning radar. The clutter of space-based early warning radar exhibits tight coupling in the azimuth-elevation-Doppler domain due to the high speed of satellites and the Earth’s rotation. As a result, conventional Space-Time Adaptive Processing (STAP) suffers significant performance degradation when detecting slow moving targets. The azimuth-elevation-Doppler three-dimensional STAP method provides the ability to decouple clutter and thus can achieve sub-optimal performance for clutter suppression. However, in contrast to the situation in non-sidelooking airborne early warning radar, this method requires large system degrees of freedom when applied to space-based early warning radar. Therefore, in practice, both the computational load and the sample requirement are too large to meet. In this study, the space-time signal model of the planar array for space-based early warning radar is first constructed. Then, the tight coupling characteristic of clutter in the azimuth-elevation-Doppler domain is analyzed in detail. On this basis, a novel three-dimensional STAP method with reduced degrees of freedom with factored structure is proposed. The sidelobe clutter is first suppressed via amplitude taper in azimuth, and the mainlobe clutter responding to each ambiguous range is further canceled by adaptive processing in the elevation-Doppler domain. The simulation results show that the proposed method can achieve sub-optimal performance under low computational load and limited sample conditions. Therefore, the proposed method is suitable for practical application in space-based early warning radar.
Synthetic Aperture Radar
Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance. Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance.
Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations. Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations.
SAR three-Dimensional (3D) imaging of complex structural facilities is an important and challenging issue in SAR imaging. The existing 3D SAR imaging relies on multiple channels or flights in the elevation direction, exerting high demands on the radar system or data acquisition. This paper proposes a 3D imaging method for complex structural facilities without a prior model, and the full-aspect 3D image of the entire scene in the region with unknown prior information can be obtained solely using a single flight. This method completely utilizes the full-aspect observation as well as the layover and elevation ambiguity resolving capabilities of circular SAR without requiring target pre-modeling and 3D imaging grid construction. Moreover, the method is suitable for fine 3D imaging complex structural facilities in large areas. Significant progress has been made in the practical technology of radar 3D imaging. The full-aspect 3D radar images of the FAST radio telescope are obtained for the first time with the proposed method, thus verifying the correctness and effectiveness of our theory and method. SAR three-Dimensional (3D) imaging of complex structural facilities is an important and challenging issue in SAR imaging. The existing 3D SAR imaging relies on multiple channels or flights in the elevation direction, exerting high demands on the radar system or data acquisition. This paper proposes a 3D imaging method for complex structural facilities without a prior model, and the full-aspect 3D image of the entire scene in the region with unknown prior information can be obtained solely using a single flight. This method completely utilizes the full-aspect observation as well as the layover and elevation ambiguity resolving capabilities of circular SAR without requiring target pre-modeling and 3D imaging grid construction. Moreover, the method is suitable for fine 3D imaging complex structural facilities in large areas. Significant progress has been made in the practical technology of radar 3D imaging. The full-aspect 3D radar images of the FAST radio telescope are obtained for the first time with the proposed method, thus verifying the correctness and effectiveness of our theory and method.