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Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.
Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.
Convolutional neural network (CNN) is widely used for image target classifications in synthetic aperture radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and trustworthiness. The class activation mapping (CAM) method is often used to visualize the decision region of the CNN model. However, existing methods are primarily based on either channel-level or space-level class activation weights, and their research progress is still in its infancy regarding more complex SAR image datasets. Based on this, this paper proposes a CNN model visualization method for SAR images, considering the feature extraction ability of neurons and their current network decisions. Initially, neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field. Further, a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed, which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images. Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.
Convolutional neural network (CNN) is widely used for image target classifications in synthetic aperture radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and trustworthiness. The class activation mapping (CAM) method is often used to visualize the decision region of the CNN model. However, existing methods are primarily based on either channel-level or space-level class activation weights, and their research progress is still in its infancy regarding more complex SAR image datasets. Based on this, this paper proposes a CNN model visualization method for SAR images, considering the feature extraction ability of neurons and their current network decisions. Initially, neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field. Further, a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed, which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images. Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
The global scattering-center model is a high-performance electromagnetic scattering parametric model for complex targets in an optical region. The traditional methods for constructing global scattering models are usually based on candidate-point screening and clustering and are prone to producing false scattering centers and ignoring actual scattering centers. To address this issue, this study proposes a novel modeling method based on the spectral peak analysis of the target electromagnetic scattering intensity field. First, the three-dimensional (3D) electromagnetic scattering intensity field of the target is estimated based on the multiperspective, one-dimensional scattering-center parameters of the target using the RANdom SAmple Consensus (RANSAC) and Parzen window methods. Next, the positions of the global 3D scattering centers are determined through spectral peak analysis, scattering-center association, and multivision measurement fusion. Finally, the scattering coefficients and type parameters of the global scattering centers are estimated after the visibility of the global scattering center is corrected through binary image morphological processing. Simulation results demonstrate that the global scattering center model extracted using this method, which is highly consistent with the geometrical structure of the target, achieves higher expression accuracy while using fewer scattering centers than those used in traditional methods.
The global scattering-center model is a high-performance electromagnetic scattering parametric model for complex targets in an optical region. The traditional methods for constructing global scattering models are usually based on candidate-point screening and clustering and are prone to producing false scattering centers and ignoring actual scattering centers. To address this issue, this study proposes a novel modeling method based on the spectral peak analysis of the target electromagnetic scattering intensity field. First, the three-dimensional (3D) electromagnetic scattering intensity field of the target is estimated based on the multiperspective, one-dimensional scattering-center parameters of the target using the RANdom SAmple Consensus (RANSAC) and Parzen window methods. Next, the positions of the global 3D scattering centers are determined through spectral peak analysis, scattering-center association, and multivision measurement fusion. Finally, the scattering coefficients and type parameters of the global scattering centers are estimated after the visibility of the global scattering center is corrected through binary image morphological processing. Simulation results demonstrate that the global scattering center model extracted using this method, which is highly consistent with the geometrical structure of the target, achieves higher expression accuracy while using fewer scattering centers than those used in traditional methods.
Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.
Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.
Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.
Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.
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2023, 12(4): 657-683.
Since the introduction of Maxwell’s equations in the 19th century, computational electromagnetics has dramatically increased development. This growth can be attributed to the evolution of numerical algorithms, such as the finite difference method, finite element method, method of moments, and high-frequency approximation methods. These numerical techniques have become a crucial foundation of modern electronic and information engineering. Artificial intelligence has recently witnessed considerable development in electromagnetics; the rapid growth within this field owes itself to its robust modeling and inferential capability. This advancement has given rise to the emerging field of intelligent electromagnetic computing, which has captured the attention of numerous researchers. Remarkable achievements include electromagnetic modeling and simulation, analysis and synthesis of new electromagnetic materials and devices, and detection and perception. These contributions have injected fresh insights into the realm of electromagnetics. This paper discusses recent advances in intelligent electromagnetic computing to highlight new perspectives and avenues in research in this emerging field.
Since the introduction of Maxwell’s equations in the 19th century, computational electromagnetics has dramatically increased development. This growth can be attributed to the evolution of numerical algorithms, such as the finite difference method, finite element method, method of moments, and high-frequency approximation methods. These numerical techniques have become a crucial foundation of modern electronic and information engineering. Artificial intelligence has recently witnessed considerable development in electromagnetics; the rapid growth within this field owes itself to its robust modeling and inferential capability. This advancement has given rise to the emerging field of intelligent electromagnetic computing, which has captured the attention of numerous researchers. Remarkable achievements include electromagnetic modeling and simulation, analysis and synthesis of new electromagnetic materials and devices, and detection and perception. These contributions have injected fresh insights into the realm of electromagnetics. This paper discusses recent advances in intelligent electromagnetic computing to highlight new perspectives and avenues in research in this emerging field.
2023, 12(4): 684-695.
In the article, the concept and system architecture of open phased array is elaborated systematically and comprehensively for the first time. Open phased array has the characteristics of virtualized resources, software-defined applications, and modular hardware architecture. Furthermore, it can adapt to current and future rapidly evolving operating tasks, operating environments, and bearing platforms. The open phased array will dominate the mainstream of the next-generation phased array system, with broad application prospects in radar, communication, electronic warfare, and other fields. This article presents the development requirements and concept of open phased array systems and summarizes their development history. The hierarchical architecture of an open phased array system is depicted in detail, and the design concept and method are comprehensively introduced in terms of hardware, resource, and application layers. Furthermore, key features of resource virtualization and processing reconfiguration of open phased array systems are introduced, and key technologies supporting open phased array realization are enumerated, which will help to develop a new generation of radio frequency systems.
In the article, the concept and system architecture of open phased array is elaborated systematically and comprehensively for the first time. Open phased array has the characteristics of virtualized resources, software-defined applications, and modular hardware architecture. Furthermore, it can adapt to current and future rapidly evolving operating tasks, operating environments, and bearing platforms. The open phased array will dominate the mainstream of the next-generation phased array system, with broad application prospects in radar, communication, electronic warfare, and other fields. This article presents the development requirements and concept of open phased array systems and summarizes their development history. The hierarchical architecture of an open phased array system is depicted in detail, and the design concept and method are comprehensively introduced in terms of hardware, resource, and application layers. Furthermore, key features of resource virtualization and processing reconfiguration of open phased array systems are introduced, and key technologies supporting open phased array realization are enumerated, which will help to develop a new generation of radio frequency systems.
2023, 12(4): 696-712.
Intelligent radar image recognition based on Deep Neural Networks (DNN) has become an important topic in radar information processing. However, DNN models are susceptible to adversarial attacks. Malicious attackers can cause intelligent image recognition models to make incorrect predictions, considerably reducing their recognition accuracy and robustness. This article reviews recent research progress on intelligent radar image recognition countermeasures. Then it summarizes the adversarial attack methods on one/two-dimensional radar image recognition models and adversarial defense methods. Finally, it discusses five open questions worthy of in-depth research in intelligent radar image recognition countermeasures.
Intelligent radar image recognition based on Deep Neural Networks (DNN) has become an important topic in radar information processing. However, DNN models are susceptible to adversarial attacks. Malicious attackers can cause intelligent image recognition models to make incorrect predictions, considerably reducing their recognition accuracy and robustness. This article reviews recent research progress on intelligent radar image recognition countermeasures. Then it summarizes the adversarial attack methods on one/two-dimensional radar image recognition models and adversarial defense methods. Finally, it discusses five open questions worthy of in-depth research in intelligent radar image recognition countermeasures.
2023, 12(4): 713-737.
Specific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space from a one-dimensional time series, preserving the nonlinear dynamic characteristics of the original system. The integration of phase space reconstruction into SEI began in 2007. However, due to the recent and diverse nature of research focused on PSR-based SEI methods, it is challenging to establish a clear context for its development. To address this issue, this paper aims to systematically summarize SEI methods based on phase space reconstruction. First, we introduce phase space reconstruction technology and emphasize the necessity and feasibility of applying it in SEI. Next, we present a comprehensive framework, classification, application, and comparison of PSR-based SEI methods. Simulation experiments demonstrated that PSR-based SEI methods can effectively describe the non-idealities of emitter hardware components and accomplish the target identification task. In addition, we verify that feature fusion enhances the algorithm’s robustness. Finally, we summarize the limitations of existing methods and outline prospects for future development.
Specific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space from a one-dimensional time series, preserving the nonlinear dynamic characteristics of the original system. The integration of phase space reconstruction into SEI began in 2007. However, due to the recent and diverse nature of research focused on PSR-based SEI methods, it is challenging to establish a clear context for its development. To address this issue, this paper aims to systematically summarize SEI methods based on phase space reconstruction. First, we introduce phase space reconstruction technology and emphasize the necessity and feasibility of applying it in SEI. Next, we present a comprehensive framework, classification, application, and comparison of PSR-based SEI methods. Simulation experiments demonstrated that PSR-based SEI methods can effectively describe the non-idealities of emitter hardware components and accomplish the target identification task. In addition, we verify that feature fusion enhances the algorithm’s robustness. Finally, we summarize the limitations of existing methods and outline prospects for future development.
2023, 12(4): 738-761.
The radar seeker is the core equipment for the terminal guidance of precision-guided weapons. It has significant benefits, such as long range and weather resistance, and plays an important role in ensuring the accuracy of missile strikes. Sea corner reflectors have excellent characteristics, such as high scattering similarity of ship targets and combat effectiveness ratio, and they have emerged as one of the primary sources of interference for radar seekers with major consequences for radar detection performance. Therefore, a difficult and critical issue in ensuring the accuracy of radar seekers is accurately and efficiently identifying sea corner reflectors. Research on the electromagnetic scattering characteristics of corner reflectors is the foundation for improving radar identification capability. This paper first introduces sea corner reflector equipment and its tactical application. The research progress in elucidating the electromagnetic scattering characteristics of sea corner reflectors is then summarized. In addition, the research achievements in radar technology for identifying sea corner reflectors are summarized, and the characteristics of existing problems pertaining to various methods are presented. Simultaneously, their future development trends of the technology are discussed.
The radar seeker is the core equipment for the terminal guidance of precision-guided weapons. It has significant benefits, such as long range and weather resistance, and plays an important role in ensuring the accuracy of missile strikes. Sea corner reflectors have excellent characteristics, such as high scattering similarity of ship targets and combat effectiveness ratio, and they have emerged as one of the primary sources of interference for radar seekers with major consequences for radar detection performance. Therefore, a difficult and critical issue in ensuring the accuracy of radar seekers is accurately and efficiently identifying sea corner reflectors. Research on the electromagnetic scattering characteristics of corner reflectors is the foundation for improving radar identification capability. This paper first introduces sea corner reflector equipment and its tactical application. The research progress in elucidating the electromagnetic scattering characteristics of sea corner reflectors is then summarized. In addition, the research achievements in radar technology for identifying sea corner reflectors are summarized, and the characteristics of existing problems pertaining to various methods are presented. Simultaneously, their future development trends of the technology are discussed.
2023, 12(4): 762-775.
Feature-based detection methods are often employed to address the challenges related to small-target detection in sea clutter. These methods determine the presence or absence of a target based on whether the feature value falls within a certain judgment region. However, such methods often overlook the temporal information between features. In fact, the temporal correlation between historical and current frame data can provide valuable a priori information, thereby enabling the calculation of the feature value of the current frame. To this end, this paper proposes a novel method for time-series modeling and prediction of radar echoes using an Auto-Regressive (AR) model in the feature domain, leveraging a priori information from historical frame features. To verify the feasibility of AR modeling and prediction of feature sequences, the AR model was first employed in the modeling and 1-step prediction analysis of Average Amplitude (AA), Relative Doppler Peak Height (RDPH), and Frequency Peak-to-Average Ratio (FPAR) feature sequences. Next, a technique for extracting feature values by utilizing the temporal information of historical frame features as a priori information was proposed. Based on this approach, a small-target detection method predicated on three-feature prediction, which can effectively utilize the temporal information of historical frame features for AA, RDPH, and FPAR, was proposed. Finally, the validity of the proposed method was verified using a measured data set.
Feature-based detection methods are often employed to address the challenges related to small-target detection in sea clutter. These methods determine the presence or absence of a target based on whether the feature value falls within a certain judgment region. However, such methods often overlook the temporal information between features. In fact, the temporal correlation between historical and current frame data can provide valuable a priori information, thereby enabling the calculation of the feature value of the current frame. To this end, this paper proposes a novel method for time-series modeling and prediction of radar echoes using an Auto-Regressive (AR) model in the feature domain, leveraging a priori information from historical frame features. To verify the feasibility of AR modeling and prediction of feature sequences, the AR model was first employed in the modeling and 1-step prediction analysis of Average Amplitude (AA), Relative Doppler Peak Height (RDPH), and Frequency Peak-to-Average Ratio (FPAR) feature sequences. Next, a technique for extracting feature values by utilizing the temporal information of historical frame features as a priori information was proposed. Based on this approach, a small-target detection method predicated on three-feature prediction, which can effectively utilize the temporal information of historical frame features for AA, RDPH, and FPAR, was proposed. Finally, the validity of the proposed method was verified using a measured data set.
2023, 12(4): 776-792.
Herein, a novel and effective method for detecting radar targets with a low signal-to-clutter ratio is proposed based on the information geometry theory. In the proposed method, the target detection problem is converted to distinguishing the target from a clutter background on a manifold. However, this is challenging when dealing with small and weak targets embedded in a complex and strong clutter background, which limits the detection performance. Therefore, to address this issue, an orthogonal projection based subband information geometry detection method is proposed. In this method, the received radar signal undergoes subband decomposition by a designed filter bank, and the robust estimation of clutter signal subspace in each subband is implemented on the matrix manifold. Subsequently, the suppression of the strong clutter is achieved through orthogonal projection based on the manifold, thereby improving the discrimination between the target and the clutter. Finally, the effectiveness of the proposed method is evaluated using simulated and real sea clutter data. The experimental results confirm that the proposed method effectively suppresses strong clutter and exhibits excellent detection performance.
Herein, a novel and effective method for detecting radar targets with a low signal-to-clutter ratio is proposed based on the information geometry theory. In the proposed method, the target detection problem is converted to distinguishing the target from a clutter background on a manifold. However, this is challenging when dealing with small and weak targets embedded in a complex and strong clutter background, which limits the detection performance. Therefore, to address this issue, an orthogonal projection based subband information geometry detection method is proposed. In this method, the received radar signal undergoes subband decomposition by a designed filter bank, and the robust estimation of clutter signal subspace in each subband is implemented on the matrix manifold. Subsequently, the suppression of the strong clutter is achieved through orthogonal projection based on the manifold, thereby improving the discrimination between the target and the clutter. Finally, the effectiveness of the proposed method is evaluated using simulated and real sea clutter data. The experimental results confirm that the proposed method effectively suppresses strong clutter and exhibits excellent detection performance.
2023, 12(4): 793-803.
Radar echo modeling based on dynamics and kinematics serves as the theoretical basis for micro-Doppler characteristic analysis and projectile parameter extractions. First, the initial disturbance of a projectile in the straight-line ballistic segment is analyzed. Based on the dynamic equation of the projectile, an angular motion model of the projectile characterized by two circular motion modes is established. Moreover, the motion definitions of projectile spin, nutation, and precession are explained. Subsequently, the parameterized characterization of the micro-Doppler signal produced by the angular motion of the projectile is derived. Furthermore, the mapping relationship between the angular motion of the projectile and the radar echo is obtained at the signal level. Taking high-speed spin projectile and a low-speed spin tail projectile as examples, when the angular motion of the two targets are affected by the initial disturbance, the radar echo signal model of the two targets is simulated and time-frequency analysis is carried out. The validity of the theoretical analysis and the model is verified by comparing the simulation results with the measured data of the projectile. Therefore, the micro-Doppler effect theory of projectile is enriched and verified through theoretical analysis, simulation modeling, and experimental verification. This study provides theoretical and technical support for the identification and analysis of projectile motion characteristics.
Radar echo modeling based on dynamics and kinematics serves as the theoretical basis for micro-Doppler characteristic analysis and projectile parameter extractions. First, the initial disturbance of a projectile in the straight-line ballistic segment is analyzed. Based on the dynamic equation of the projectile, an angular motion model of the projectile characterized by two circular motion modes is established. Moreover, the motion definitions of projectile spin, nutation, and precession are explained. Subsequently, the parameterized characterization of the micro-Doppler signal produced by the angular motion of the projectile is derived. Furthermore, the mapping relationship between the angular motion of the projectile and the radar echo is obtained at the signal level. Taking high-speed spin projectile and a low-speed spin tail projectile as examples, when the angular motion of the two targets are affected by the initial disturbance, the radar echo signal model of the two targets is simulated and time-frequency analysis is carried out. The validity of the theoretical analysis and the model is verified by comparing the simulation results with the measured data of the projectile. Therefore, the micro-Doppler effect theory of projectile is enriched and verified through theoretical analysis, simulation modeling, and experimental verification. This study provides theoretical and technical support for the identification and analysis of projectile motion characteristics.
2023, 12(4): 804-816.
Compared with traditional Electromagnetic (EM) wave radars, vortex EM wave radars can simultaneously observe the micro-motion components projected onto the radar’s radial and perpendicular planes, providing more information for target recognition. The current research on the micro-Doppler effect of vortex EM wave radar is still in its infancy, and the extraction of three-dimensional micro-motion parameters of rotating targets has been preliminarily achieved. However, the impact of target translation was not considered. Therefore, the micro-Doppler effect of translational rotating targets in vortex EM wave radar is studied in this paper. The angular Doppler properties of translational rotating targets are derived, and a three-dimensional micro-motion parameter extraction method based on the 1/4 micro-motion period Doppler frequency shift curve is proposed. Estimation of parameters such as target rotation frequency, rotation radius, rotation vector and translational velocity vector is achieved. The correctness of angular Doppler properties and the effectiveness of parameter extraction method are verified by simulations.
Compared with traditional Electromagnetic (EM) wave radars, vortex EM wave radars can simultaneously observe the micro-motion components projected onto the radar’s radial and perpendicular planes, providing more information for target recognition. The current research on the micro-Doppler effect of vortex EM wave radar is still in its infancy, and the extraction of three-dimensional micro-motion parameters of rotating targets has been preliminarily achieved. However, the impact of target translation was not considered. Therefore, the micro-Doppler effect of translational rotating targets in vortex EM wave radar is studied in this paper. The angular Doppler properties of translational rotating targets are derived, and a three-dimensional micro-motion parameter extraction method based on the 1/4 micro-motion period Doppler frequency shift curve is proposed. Estimation of parameters such as target rotation frequency, rotation radius, rotation vector and translational velocity vector is achieved. The correctness of angular Doppler properties and the effectiveness of parameter extraction method are verified by simulations.
2023, 12(4): 817-831.
When underwater acoustic signals propagate to the water surface, the acoustic impedance difference between water and air leads to transverse microamplitude waves on the water surface. These waves carry vibration frequencies containing relevant information about the sound source. Radar systems detect the slight displacement of the target through the phase difference between the target echoes; hence, radar systems can be used to detect small displacement changes on the water surface, thereby obtaining the water-surface vibration signal and subsequently inverting the underwater sound source information. In this study, we first analyzed the attenuation characteristics of underwater sound propagation and the physical model of water-surface vibration. Building upon the radar echo model for detecting acoustic water-surface vibration, we proposed a wavelet-Kalman filter signal detection method through theoretical analysis. Lastly, experiments were conducted in the large-scale comprehensive anechoic pool and the Yellow Sea water using terahertz radar for acoustic water-surface micromotion signal detection. The results demonstrate the capability of the terahertz radar to successfully detect acoustic water-surface microvibrations. The proposed method effectively filters water-surface interference and radar phase noise and extracts vibration signals. For the first time, submicron vibration signals were detected under a secondary sea state, providing a foundation for water-space transmedia information transmission and underwater vehicle detection.
When underwater acoustic signals propagate to the water surface, the acoustic impedance difference between water and air leads to transverse microamplitude waves on the water surface. These waves carry vibration frequencies containing relevant information about the sound source. Radar systems detect the slight displacement of the target through the phase difference between the target echoes; hence, radar systems can be used to detect small displacement changes on the water surface, thereby obtaining the water-surface vibration signal and subsequently inverting the underwater sound source information. In this study, we first analyzed the attenuation characteristics of underwater sound propagation and the physical model of water-surface vibration. Building upon the radar echo model for detecting acoustic water-surface vibration, we proposed a wavelet-Kalman filter signal detection method through theoretical analysis. Lastly, experiments were conducted in the large-scale comprehensive anechoic pool and the Yellow Sea water using terahertz radar for acoustic water-surface micromotion signal detection. The results demonstrate the capability of the terahertz radar to successfully detect acoustic water-surface microvibrations. The proposed method effectively filters water-surface interference and radar phase noise and extracts vibration signals. For the first time, submicron vibration signals were detected under a secondary sea state, providing a foundation for water-space transmedia information transmission and underwater vehicle detection.
2023, 12(4): 832-848.
Bistatic interferometric Synthetic Aperture Radar (SAR) overcomes the baseline length limit of the configuration of single-station interferometric SAR with two antennas and has become the primary method of terrain mapping using spaceborne interferometric SAR. To reduce the cost of surveying and mapping while promoting the development and application of Unmanned Aerial Vehicle (UAV)-borne bistatic interferometric SAR, the Aerospace Information Research Institute, Chinese Academy of Sciences took the lead in designing and developing a UAV-borne bistatic interferometric SAR processing system and performed flight experiments at Bailing Airport in Inner Mongolia. Herein, the system design, composition, and performance are introduced, and the scheme and implementation of the first flight experiment, along with the preliminary data processing results, are presented. In addition, the key performance metrics of the system, such as 0.5 m-elevation measurement accuracy, are verified in this study. The system serves as a foundation for future research topics, such as distributed InSAR using a multiaviation platform and tomography data acquisition and processing.
Bistatic interferometric Synthetic Aperture Radar (SAR) overcomes the baseline length limit of the configuration of single-station interferometric SAR with two antennas and has become the primary method of terrain mapping using spaceborne interferometric SAR. To reduce the cost of surveying and mapping while promoting the development and application of Unmanned Aerial Vehicle (UAV)-borne bistatic interferometric SAR, the Aerospace Information Research Institute, Chinese Academy of Sciences took the lead in designing and developing a UAV-borne bistatic interferometric SAR processing system and performed flight experiments at Bailing Airport in Inner Mongolia. Herein, the system design, composition, and performance are introduced, and the scheme and implementation of the first flight experiment, along with the preliminary data processing results, are presented. In addition, the key performance metrics of the system, such as 0.5 m-elevation measurement accuracy, are verified in this study. The system serves as a foundation for future research topics, such as distributed InSAR using a multiaviation platform and tomography data acquisition and processing.
2023, 12(4): 849-859.
Sparse Aperture-Inverse Synthetic Aperture Radar (SA-ISAR) imaging methods aim to reconstruct high-quality ISAR images from the corresponding incomplete ISAR echoes. The existing SA-ISAR imaging methods can be roughly divided into two categories: model-based and deep learning-based methods. Model-based SA-ISAR methods comprise physical ISAR imaging models based on explicit mathematical formulations. However, due to the high nonconvexity and ill-posedness of the SA-ISAR problem, model-based methods are often ineffective compared with deep learning-based methods. Meanwhile, the performance of the existing deep learning-based methods depends on the quality and quantity of the training data, which are neither sufficient nor precisely labeled in space target SA-ISAR imaging tasks. To address these issues, we propose a metalearning-based SA-ISAR imaging method for space target ISAR imaging tasks. The proposed method comprises two primary modules: the learning-aided alternating minimization module and the metalearning-based optimization module. The learning-aided alternating minimization module retains the explicit ISAR imaging formulations, guaranteeing physical interpretability without data dependency. The metalearning-based optimization module incorporates a non-greedy strategy to enhance convergence performance, ensuring the ability to escape from poor local modes during optimization. Extensive experiments validate that the proposed algorithm demonstrates superior performance, excellent generalization capability, and high efficiency, despite the lack of prior training or access to labeled training samples, compared to existing methods.
Sparse Aperture-Inverse Synthetic Aperture Radar (SA-ISAR) imaging methods aim to reconstruct high-quality ISAR images from the corresponding incomplete ISAR echoes. The existing SA-ISAR imaging methods can be roughly divided into two categories: model-based and deep learning-based methods. Model-based SA-ISAR methods comprise physical ISAR imaging models based on explicit mathematical formulations. However, due to the high nonconvexity and ill-posedness of the SA-ISAR problem, model-based methods are often ineffective compared with deep learning-based methods. Meanwhile, the performance of the existing deep learning-based methods depends on the quality and quantity of the training data, which are neither sufficient nor precisely labeled in space target SA-ISAR imaging tasks. To address these issues, we propose a metalearning-based SA-ISAR imaging method for space target ISAR imaging tasks. The proposed method comprises two primary modules: the learning-aided alternating minimization module and the metalearning-based optimization module. The learning-aided alternating minimization module retains the explicit ISAR imaging formulations, guaranteeing physical interpretability without data dependency. The metalearning-based optimization module incorporates a non-greedy strategy to enhance convergence performance, ensuring the ability to escape from poor local modes during optimization. Extensive experiments validate that the proposed algorithm demonstrates superior performance, excellent generalization capability, and high efficiency, despite the lack of prior training or access to labeled training samples, compared to existing methods.
2023, 12(4): 860-881.
The performance of machine learning-based radar target recognition models is determined by the respective model and data to be analyzed. Currently, radar target recognition performance evaluation is based on accuracy metrics, but this method does not include the evaluation metrics regarding the impact of data quality on recognition performance. Data separability describes the degree of mixture of samples from different categories. Furthermore, the data separability metric is independent of the model recognition process. By incorporating it into the recognition evaluation process, recognition difficulty can be quantified, and a benchmark for recognition results can be provided in advance. Therefore, in this paper, we propose a data separability metric based on the rate-distortion theory. Extensive experiments on multiple simulated datasets demonstrated that the proposed metric can compare the separability of multivariate Gaussian datasets. Furthermore, by combining it with the Gaussian mixture model, the designed metric method could overcome the limitation of the rate-distortion function, capture the data’s local separable characteristics, and improve the evaluation accuracy of the overall data separability. Subsequently, we applied the proposed metric to evaluate the recognition difficulty in real datasets, the results of which validated its strong correlation with average recognition accuracy. In the experiments on evaluating the effectiveness of convolutional neural network modules, we first quantified and analyzed the separability trend of the feature extracted by each module during the testing phase. Further, we incorporated the proposed metric as a feature separability loss during the training phase to participate in the network optimization process, guiding the network to extract a more separable feature. This paper provides a new perspective for evaluating and improving the neural network recognition performance in terms of feature separability.
The performance of machine learning-based radar target recognition models is determined by the respective model and data to be analyzed. Currently, radar target recognition performance evaluation is based on accuracy metrics, but this method does not include the evaluation metrics regarding the impact of data quality on recognition performance. Data separability describes the degree of mixture of samples from different categories. Furthermore, the data separability metric is independent of the model recognition process. By incorporating it into the recognition evaluation process, recognition difficulty can be quantified, and a benchmark for recognition results can be provided in advance. Therefore, in this paper, we propose a data separability metric based on the rate-distortion theory. Extensive experiments on multiple simulated datasets demonstrated that the proposed metric can compare the separability of multivariate Gaussian datasets. Furthermore, by combining it with the Gaussian mixture model, the designed metric method could overcome the limitation of the rate-distortion function, capture the data’s local separable characteristics, and improve the evaluation accuracy of the overall data separability. Subsequently, we applied the proposed metric to evaluate the recognition difficulty in real datasets, the results of which validated its strong correlation with average recognition accuracy. In the experiments on evaluating the effectiveness of convolutional neural network modules, we first quantified and analyzed the separability trend of the feature extracted by each module during the testing phase. Further, we incorporated the proposed metric as a feature separability loss during the training phase to participate in the network optimization process, guiding the network to extract a more separable feature. This paper provides a new perspective for evaluating and improving the neural network recognition performance in terms of feature separability.
2023, 12(4): 882-891.
Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.
Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.
2023, 12(4): 892-905.
As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset.
As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset.
2023, 12(4): 906-922.
This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.
This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.
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Announcements
More- Call for Papers of the 15th Annual Radar Conference of China
- High-resolution SAR Ship Detection Dataset-2.0 are officially released
- Call for Papers of Special Issue on Intelligent Processing of Microwave Remote Sensing Information
- Call for Papers of Radars Electromagnetic Scattering Imaging and Recognition
- Call for Papers of Special Issue on New technology of radar electromagnetic scattering imaging recognition
- Notice on the Recruitment of Academic Editors of "Journal of Radars"
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New Progress in Intelligent Electromagnetic Sensing System
- Editorial Comments of Special Issue on the Information Metamaterials and Their Radar Applications
- The Radar and Microwave Vision Sub-Forum and the 1st Journal of Radars Lecture was held at Nanjing
- The First International Airborne Array Synthetic Aperture Radar 3D Imaging
- The Spaceborne Synthetic Aperture Radar Images of Gaofen-3
- Researcher Chibiao Ding presided over the Radar and Microwave Vision Sub-Forum and gave a special lecture
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