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1
2024, 13(2): 307-330.
Abstract
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Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image interpretation. With the introduction of deep learning, the performance of aircraft detection and recognition, which is based on SAR imagery, has considerably improved. This paper combines the expertise gathered by our research team on the theory, algorithms, and applications of SAR image-based target detection and recognition, particularly aircraft. Additionally, this paper presents a comprehensive review of deep learning-powered aircraft detection and recognition based on SAR imagery. This review includes a detailed analysis of the aircraft target characteristics and current challenges associated with SAR image-based detection and recognition. Furthermore, the review summarizes the latest research advancements, characteristics, and application scenarios of various technologies and collates public datasets and performance evaluation metrics. Finally, several challenges and potential research prospects are discussed.
Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image interpretation. With the introduction of deep learning, the performance of aircraft detection and recognition, which is based on SAR imagery, has considerably improved. This paper combines the expertise gathered by our research team on the theory, algorithms, and applications of SAR image-based target detection and recognition, particularly aircraft. Additionally, this paper presents a comprehensive review of deep learning-powered aircraft detection and recognition based on SAR imagery. This review includes a detailed analysis of the aircraft target characteristics and current challenges associated with SAR image-based detection and recognition. Furthermore, the review summarizes the latest research advancements, characteristics, and application scenarios of various technologies and collates public datasets and performance evaluation metrics. Finally, several challenges and potential research prospects are discussed.
2
2024, 13(3): 539-553.
Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.
Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.
3
2024, 13(3): 554-564.
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.
4
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.
5
2023, 12(5): 923-970.
As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.
As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.
6
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.
7
2023, 12(3): 471-499.
Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.
Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.
8
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.
9
2024, 13(2): 285-306.
With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.
With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.
10
2024, 13(3): 501-524.
Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.
Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.
11
2024, 13(1): 23-45.
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.
12
2024, 13(1): 1-22.
The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
13
2020, 9(5): 803-827.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
14
2020, 9(1): 1-33.
Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
15
2023, 12(1): 197-213.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
16
2023, 12(6): 1229-1248.
Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.
Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.
17
2024, 13(2): 359-373.
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.
18
2024, 13(1): 46-67.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
19
2020, 9(5): 753-764.
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
20
2022, 11(6): 974-1002.
As the electromagnetic spectrum becomes a key operational domain in modern warfare, radars will face a more complex, dexterous, and smarter electromagnetic interference environment in future military operations. Cognitive Intelligent Radar (CIR) has become one of the key development directions in the field of radar technology because it has the capabilities of active environmental perception, arbitrary transmit and receive design, intelligent signal processing, and resource scheduling, therefore, can adapt to the complex and changeable battlefield electromagnetic confrontation environment. In this study, the CIR is decomposed into four functional modules: cognitive transmitting, cognitive receiving, intelligent signal processing, and intelligent resource scheduling. Then, the antijamming principle of each link (i.e., interference perception, transmit design, receive design, signal processing, and resource scheduling) of CIR is elucidated. Finally, we summarize the representative literature in recent years and analyze the technological development trend in this field to provide the necessary reference and basis for future technological research.
As the electromagnetic spectrum becomes a key operational domain in modern warfare, radars will face a more complex, dexterous, and smarter electromagnetic interference environment in future military operations. Cognitive Intelligent Radar (CIR) has become one of the key development directions in the field of radar technology because it has the capabilities of active environmental perception, arbitrary transmit and receive design, intelligent signal processing, and resource scheduling, therefore, can adapt to the complex and changeable battlefield electromagnetic confrontation environment. In this study, the CIR is decomposed into four functional modules: cognitive transmitting, cognitive receiving, intelligent signal processing, and intelligent resource scheduling. Then, the antijamming principle of each link (i.e., interference perception, transmit design, receive design, signal processing, and resource scheduling) of CIR is elucidated. Finally, we summarize the representative literature in recent years and analyze the technological development trend in this field to provide the necessary reference and basis for future technological research.
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