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2025, 14(5): 1115-1141.
Abstract
20404KB
The bistatic Synthetic Aperture Radar (SAR) system, which employs spatially separated transmitting and receiving platforms, provides high-resolution imaging of terrestrial and maritime scenes and targets in complex environments. Its advantages include flexible configuration, strong concealment capabilities, high interference resistance, and comprehensive target information acquisition, making it valuable in high-precision remote sensing mapping, covert imaging, and precision strikes. Image processing is critical for obtaining high-resolution Bistatic SAR (BiSAR) images. However, the echo model and characteristics of BiSAR substantially differ from those of traditional monostatic SAR, necessitating specialized image processing methods tailored to various operational modes and configurations. This study examines key challenges and solutions for several BiSAR configurations, including airborne BiSAR, BiSAR with high-speed and highly maneuverable platforms, spaceborne heterogeneous BiSAR, and spaceborne homogeneous BiSAR. This study also addresses motion compensation approaches and moving target imaging in BiSAR systems, reviews relevant domestic and international research advancements, and provides an outlook on future trends in BiSAR image processing.
The bistatic Synthetic Aperture Radar (SAR) system, which employs spatially separated transmitting and receiving platforms, provides high-resolution imaging of terrestrial and maritime scenes and targets in complex environments. Its advantages include flexible configuration, strong concealment capabilities, high interference resistance, and comprehensive target information acquisition, making it valuable in high-precision remote sensing mapping, covert imaging, and precision strikes. Image processing is critical for obtaining high-resolution Bistatic SAR (BiSAR) images. However, the echo model and characteristics of BiSAR substantially differ from those of traditional monostatic SAR, necessitating specialized image processing methods tailored to various operational modes and configurations. This study examines key challenges and solutions for several BiSAR configurations, including airborne BiSAR, BiSAR with high-speed and highly maneuverable platforms, spaceborne heterogeneous BiSAR, and spaceborne homogeneous BiSAR. This study also addresses motion compensation approaches and moving target imaging in BiSAR systems, reviews relevant domestic and international research advancements, and provides an outlook on future trends in BiSAR image processing.
2025, 14(5): 1142-1152.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
2025, 14(5): 1153-1169.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
2025, 14(5): 1170-1195.
Bistatic Inverse Synthetic Aperture Radar (Bi-ISAR) imaging has a broad application in air and space target detection and recognition. However, due to the complexity and variability of the bistatic configuration, the Bi-ISAR imaging performance under different observation configurations varies greatly. There may even be imaging arcs under some observation configurations in which the two-dimensional imaging results cannot be acquired. Therefore, it is quite essential to find out the useful Bi-ISAR imaging arcs quickly and accurately. Aiming at the need of fast and optimal selection of imaging boundary and imaging arcs for aerial moving targets, a boundary analysis and fast imaging arc selection method for Bi-ISAR imaging with bistatic angle derivative constraint is proposed. Firstly, the Bi-ISAR imaging model of moving target is constructed, and the expression of bistatic slant range history related to the bistatic angle derivative is derived. Then, the boundary of the Bi-ISAR imaging performance is theoretically analyzed from the dimensions of Range Cell Migration (RCM) and Azimuth-Quadratic Phase (AQP), and the corresponding constraints are figured out. Finally, based on the minimum fusion criterion, the Bi-ISAR imaging boundary constrained by the bistatic angle derivative is presented. Moreover, it is proved that the boundary constraint of the bistatic angle derivative is equivalent to the selection of the Bi-ISAR imaging arc. The processing results of both simulation data and measured data verify the effectiveness of the proposed method.
Bistatic Inverse Synthetic Aperture Radar (Bi-ISAR) imaging has a broad application in air and space target detection and recognition. However, due to the complexity and variability of the bistatic configuration, the Bi-ISAR imaging performance under different observation configurations varies greatly. There may even be imaging arcs under some observation configurations in which the two-dimensional imaging results cannot be acquired. Therefore, it is quite essential to find out the useful Bi-ISAR imaging arcs quickly and accurately. Aiming at the need of fast and optimal selection of imaging boundary and imaging arcs for aerial moving targets, a boundary analysis and fast imaging arc selection method for Bi-ISAR imaging with bistatic angle derivative constraint is proposed. Firstly, the Bi-ISAR imaging model of moving target is constructed, and the expression of bistatic slant range history related to the bistatic angle derivative is derived. Then, the boundary of the Bi-ISAR imaging performance is theoretically analyzed from the dimensions of Range Cell Migration (RCM) and Azimuth-Quadratic Phase (AQP), and the corresponding constraints are figured out. Finally, based on the minimum fusion criterion, the Bi-ISAR imaging boundary constrained by the bistatic angle derivative is presented. Moreover, it is proved that the boundary constraint of the bistatic angle derivative is equivalent to the selection of the Bi-ISAR imaging arc. The processing results of both simulation data and measured data verify the effectiveness of the proposed method.
2025, 14(5): 1196-1214.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Method of Multiplier (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Method of Multiplier (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
2025, 14(5): 1215-1229.
Conventional spaceborne monostatic radar systems incur huge engineering costs to achieve small moving-target detection and low anti-interference ability. By manipulating the transmitter-receiver separation in a spaceborne bistatic radar system, the target radar cross section can be effectively improved by adopting a configuration with a large azimuth bistatic angle, and the anti-interference ability can be improved because the receiver does not transmit signals. However, the characteristics of the background clutter echo in a spaceborne bistatic radar system differ drastically from those in a spaceborne monostatic radar system because of the transmitter-receiver separation in the former. To overcome the limitations of existing empirical clutter scattering coefficient models, which typically do not capture the variation of scattering coefficient with azimuth bistatic angle, this study proposes a semiempirical bistatic clutter scattering coefficient model based on the two-scale model. In the proposed model, an empirical clutter backscattering coefficient model can be converted to a bistatic clutter scattering coefficient model based on electromagnetic scattering theories, and the bistatic scattering coefficient is further modified based on the two-scale model. The proposed model was validated using real measured data of bistatic clutter scattering coefficients obtained from existing literature. Using the proposed model, clutter suppression performance under different azimuth bistatic angles was analyzed by employing space-time adaptive processing in spaceborne bistatic radar systems. Reportedly, under HH polarization, the clutter suppression performance was relatively good when the azimuth bistatic angle was 30°～130°, whereas the clutter suppression performance was considerably affected by large-power main-lobe clutter when the azimuth bistatic angle was >150°.
Conventional spaceborne monostatic radar systems incur huge engineering costs to achieve small moving-target detection and low anti-interference ability. By manipulating the transmitter-receiver separation in a spaceborne bistatic radar system, the target radar cross section can be effectively improved by adopting a configuration with a large azimuth bistatic angle, and the anti-interference ability can be improved because the receiver does not transmit signals. However, the characteristics of the background clutter echo in a spaceborne bistatic radar system differ drastically from those in a spaceborne monostatic radar system because of the transmitter-receiver separation in the former. To overcome the limitations of existing empirical clutter scattering coefficient models, which typically do not capture the variation of scattering coefficient with azimuth bistatic angle, this study proposes a semiempirical bistatic clutter scattering coefficient model based on the two-scale model. In the proposed model, an empirical clutter backscattering coefficient model can be converted to a bistatic clutter scattering coefficient model based on electromagnetic scattering theories, and the bistatic scattering coefficient is further modified based on the two-scale model. The proposed model was validated using real measured data of bistatic clutter scattering coefficients obtained from existing literature. Using the proposed model, clutter suppression performance under different azimuth bistatic angles was analyzed by employing space-time adaptive processing in spaceborne bistatic radar systems. Reportedly, under HH polarization, the clutter suppression performance was relatively good when the azimuth bistatic angle was 30°～130°, whereas the clutter suppression performance was considerably affected by large-power main-lobe clutter when the azimuth bistatic angle was >150°.
2025, 14(5): 1230-1252.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
2025, 14(5): 1253-1275.
Bistatic Inverse Synthetic Aperture Radar (Bi-ISAR) has garnered significant attention in the military and civilian domains due to its superior stealth and antijamming capabilities. However, the changing bistatic angle during Bi-ISAR imaging causes space-variant defocusing and geometric distortion in the resulting images, thereby severely compromising the accuracy of subsequent information extraction and target recognition. To address these issues, this study proposes a fast space-variant phase error compensation and geometric correction method for Bi-ISAR imaging based on a modified Newton’s method. This method uses the image entropy of the Bi-ISAR imaging result as the cost function and introduces space-variant coefficients and rotation parameters as optimization variables to formulate an optimization equation. By modifying the traditional Newton’s method to ensure the positive definiteness of the Hessian matrix, the cost function is guaranteed to be optimized along the descent direction in each iteration. Solving this optimization equation to minimize image entropy simultaneously estimates the rotation parameters, which are then used to construct a geometric correction function and calculate the scaling factor, that is, the actual size of each grid in the image, enabling geometric correction and scaling of the final imaging result. The proposed method simultaneously corrects space-variant phase errors and geometric distortion and operates in a data-driven manner, requiring only low initial image quality. Furthermore, due to the quadratic convergence property of Newton’s method, the proposed method offers higher computational efficiency compared with other methods. Finally, the effectiveness of the proposed method is validated through the processing and comparative analysis of the point target simulation, electromagnetic calculation, and ground real target experimental data.
Bistatic Inverse Synthetic Aperture Radar (Bi-ISAR) has garnered significant attention in the military and civilian domains due to its superior stealth and antijamming capabilities. However, the changing bistatic angle during Bi-ISAR imaging causes space-variant defocusing and geometric distortion in the resulting images, thereby severely compromising the accuracy of subsequent information extraction and target recognition. To address these issues, this study proposes a fast space-variant phase error compensation and geometric correction method for Bi-ISAR imaging based on a modified Newton’s method. This method uses the image entropy of the Bi-ISAR imaging result as the cost function and introduces space-variant coefficients and rotation parameters as optimization variables to formulate an optimization equation. By modifying the traditional Newton’s method to ensure the positive definiteness of the Hessian matrix, the cost function is guaranteed to be optimized along the descent direction in each iteration. Solving this optimization equation to minimize image entropy simultaneously estimates the rotation parameters, which are then used to construct a geometric correction function and calculate the scaling factor, that is, the actual size of each grid in the image, enabling geometric correction and scaling of the final imaging result. The proposed method simultaneously corrects space-variant phase errors and geometric distortion and operates in a data-driven manner, requiring only low initial image quality. Furthermore, due to the quadratic convergence property of Newton’s method, the proposed method offers higher computational efficiency compared with other methods. Finally, the effectiveness of the proposed method is validated through the processing and comparative analysis of the point target simulation, electromagnetic calculation, and ground real target experimental data.
2025, 14(5): 1276-1293.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
2025, 14(5): 1294-1305.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
2025, 14(5): 1306-1322.
The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.
The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.
2025, 14(5): 1323-1342.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.
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