Submit Manuscript
Peer Review
Editor WorkMost Cited
（The cited data comes from the whole network and is updated monthly.）
1
2022, 11(2): 198-212.
Abstract
1701KB
Radar and communication systems are hosted on the same platform in many civilian and military applications. Traditionally, radar and communication systems are separately designed, which increases the system size, cost, and power consumption, and decreases the electromagnetic compatibility. Joint radar and communication designs, which have drawn much attention from both the academic and industrial circles, overcome these problems by implementing radar and communication systems using the same hardware. Joint radar and communications systems can be realized by resource allocation and waveform sharing. Waveform sharing schemes have become popular in recent years because they have higher spectral and power efficiency and can fundamentally avoid interference between the different systems. This paper studies the existing strategies of shared waveforms for joint radar and communications systems. The existing strategies are divided into three categories, namely: the communication waveform-based approaches, the radar waveform-based methods, and the joint design schemes. The performance bounds of the joint radar and communication systems are also reviewed to reveal the trade-off between the performance metrics of radar and communications in these systems. The potential for future research into joint radar and communication designs is also discussed.
Radar and communication systems are hosted on the same platform in many civilian and military applications. Traditionally, radar and communication systems are separately designed, which increases the system size, cost, and power consumption, and decreases the electromagnetic compatibility. Joint radar and communication designs, which have drawn much attention from both the academic and industrial circles, overcome these problems by implementing radar and communication systems using the same hardware. Joint radar and communications systems can be realized by resource allocation and waveform sharing. Waveform sharing schemes have become popular in recent years because they have higher spectral and power efficiency and can fundamentally avoid interference between the different systems. This paper studies the existing strategies of shared waveforms for joint radar and communications systems. The existing strategies are divided into three categories, namely: the communication waveform-based approaches, the radar waveform-based methods, and the joint design schemes. The performance bounds of the joint radar and communication systems are also reviewed to reveal the trade-off between the performance metrics of radar and communications in these systems. The potential for future research into joint radar and communication designs is also discussed.
2
2022, 11(3): 418-433.
Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.
Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.
3
2022, 11(3): 499-516.
A contactless health monitoring system can contribute to health assessment in daily life by reducing appliance usage and avoiding discomfort from wearing electrodes or sensors. Such contactless approaches have the potential to continuously monitor the health status of users, alert patients and health personnel in time when acute medical emergencies occur, and meet the monitoring demands of special populations, such as newborns, burn patients, and patients with infectious diseases. The Frequency-Modulated Continuous-Wave (FMCW) radar can measure the range and velocity of sensing targets and be widely applied in heart and respiration rate monitoring and fall detection. Moreover, advances in FMCW radar have enabled low-cost radar-on-chip and antenna-on-chip systems. Thus, FMCW radar has vital application value in the medical and health monitoring fields. In this study, first, we introduce the basic knowledge of the application of FMCW radar in contactless health monitoring. Then, we systematically review the advanced applications and latest papers in this field. Finally, we summarize the present situations and limitations and provide a brief outlook for the application prospects and potential future research in the field.
A contactless health monitoring system can contribute to health assessment in daily life by reducing appliance usage and avoiding discomfort from wearing electrodes or sensors. Such contactless approaches have the potential to continuously monitor the health status of users, alert patients and health personnel in time when acute medical emergencies occur, and meet the monitoring demands of special populations, such as newborns, burn patients, and patients with infectious diseases. The Frequency-Modulated Continuous-Wave (FMCW) radar can measure the range and velocity of sensing targets and be widely applied in heart and respiration rate monitoring and fall detection. Moreover, advances in FMCW radar have enabled low-cost radar-on-chip and antenna-on-chip systems. Thus, FMCW radar has vital application value in the medical and health monitoring fields. In this study, first, we introduce the basic knowledge of the application of FMCW radar in contactless health monitoring. Then, we systematically review the advanced applications and latest papers in this field. Finally, we summarize the present situations and limitations and provide a brief outlook for the application prospects and potential future research in the field.
4
2022, 11(5): 805-829.
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
5
2022, 11(1): 107-125.
Deep learning technologies have been developed rapidly in Synthetic Aperture Radar (SAR) image interpretation. The current data-driven methods neglect the latent physical characteristics of SAR; thus, the predictions are highly dependent on training data and even violate physical laws. Deep integration of the theory-driven and data-driven approaches for SAR image interpretation is of vital importance. Additionally, the data-driven methods specialize in automatically discovering patterns from a large amount of data that serve as effective complements for physical processes, whereas the integrated interpretable physical models improve the explainability of deep learning algorithms and address the data-hungry problem. This study aimed to develop physically explainable deep learning for SAR image interpretation in signals, scattering mechanisms, semantics, and applications. Strategies for blending the theory-driven and data-driven methods in SAR interpretation are proposed based on physics machine learning to develop novel learnable and explainable paradigms for SAR image interpretation. Further, recent studies on hybrid methods are reviewed, including SAR signal processing, physical characteristics, and semantic image interpretation. Challenges and future perspectives are also discussed on the basis of the research status and related studies in other fields, which can serve as inspiration.
Deep learning technologies have been developed rapidly in Synthetic Aperture Radar (SAR) image interpretation. The current data-driven methods neglect the latent physical characteristics of SAR; thus, the predictions are highly dependent on training data and even violate physical laws. Deep integration of the theory-driven and data-driven approaches for SAR image interpretation is of vital importance. Additionally, the data-driven methods specialize in automatically discovering patterns from a large amount of data that serve as effective complements for physical processes, whereas the integrated interpretable physical models improve the explainability of deep learning algorithms and address the data-hungry problem. This study aimed to develop physically explainable deep learning for SAR image interpretation in signals, scattering mechanisms, semantics, and applications. Strategies for blending the theory-driven and data-driven methods in SAR interpretation are proposed based on physics machine learning to develop novel learnable and explainable paradigms for SAR image interpretation. Further, recent studies on hybrid methods are reviewed, including SAR signal processing, physical characteristics, and semantic image interpretation. Challenges and future perspectives are also discussed on the basis of the research status and related studies in other fields, which can serve as inspiration.
6
2022, 11(2): 301-312.
To improve radar’s anti-Interrupted Sampling Repeater Jamming (ISRJ) capability, this study proposes a parallel interference suppression method based on the fractional Fourier transform, which uses the “active” anti-jamming capability of the interpulse and intrapulse frequency-agile waveform according to the characteristics of ISRJ transceiver splitting. First, the interfered sub-pulses are extracted in the time domain, and the extracted signals are sliced. Then, the narrowband filter banks are used to suppress the interference in the fractional Fourier domain. Finally, matching filter banks are constructed to achieve subpulse integration by applying segmented pulse compression. The theoretical analysis and simulation results show that the proposed method effectively suppresses multi-mainlobe interferences comprising different types of ISRJ and exhibits good anti-interference performance under a high jamming-to-signal ratio, which considerably improves the anti-jamming capability of the radar.
To improve radar’s anti-Interrupted Sampling Repeater Jamming (ISRJ) capability, this study proposes a parallel interference suppression method based on the fractional Fourier transform, which uses the “active” anti-jamming capability of the interpulse and intrapulse frequency-agile waveform according to the characteristics of ISRJ transceiver splitting. First, the interfered sub-pulses are extracted in the time domain, and the extracted signals are sliced. Then, the narrowband filter banks are used to suppress the interference in the fractional Fourier domain. Finally, matching filter banks are constructed to achieve subpulse integration by applying segmented pulse compression. The theoretical analysis and simulation results show that the proposed method effectively suppresses multi-mainlobe interferences comprising different types of ISRJ and exhibits good anti-interference performance under a high jamming-to-signal ratio, which considerably improves the anti-jamming capability of the radar.
7
2022, 11(1): 1-19.
Three-Dimensional (3D) Synthetic Aperture Radar (SAR) imaging has considerable application potential in steep-terrain mapping and target recognition in complex environments and is an important development direction in the current SAR field. To promote the development and application of the 3D SAR imaging technology, the Aerospace Information Research Institute, Chinese Academy of Sciences designed and developed an unmanned aerial vehicle-borne Microwave-Vision 3D SAR (MV3DSAR) experimental system, which provides an experimental platform for the research and verification of related technologies. Currently, the single-polarization version of the system has been developed, and the first flight experiment has been conducted in Tianjin. This study introduces the structure, performance, key technologies, and data processing of the system. This study also presents the implementation and preliminary data processing results of the first experiment, verifying the basic performance and 3D imaging capability of the system. The MV3DSAR provides a good experimental and verification platform for analyzing 3D SAR imaging algorithms and constructing 3D SAR imaging datasets.
Three-Dimensional (3D) Synthetic Aperture Radar (SAR) imaging has considerable application potential in steep-terrain mapping and target recognition in complex environments and is an important development direction in the current SAR field. To promote the development and application of the 3D SAR imaging technology, the Aerospace Information Research Institute, Chinese Academy of Sciences designed and developed an unmanned aerial vehicle-borne Microwave-Vision 3D SAR (MV3DSAR) experimental system, which provides an experimental platform for the research and verification of related technologies. Currently, the single-polarization version of the system has been developed, and the first flight experiment has been conducted in Tianjin. This study introduces the structure, performance, key technologies, and data processing of the system. This study also presents the implementation and preliminary data processing results of the first experiment, verifying the basic performance and 3D imaging capability of the system. The MV3DSAR provides a good experimental and verification platform for analyzing 3D SAR imaging algorithms and constructing 3D SAR imaging datasets.
8
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.
9
2022, 11(1): 40-51.
Conventional Synthetic Aperture Radar (SAR) can only obtain two-dimensional (2-D) azimuth-range images without accurately reflecting the three-Dimensional (3-D) scattering structure information of the targets. However, SAR Tomography (TomoSAR) is a multi-baseline interferometric measurement mode that extends the synthetic aperture principle into the elevation direction, making it possible to recover the true height of the target, thereby achieving 3-D imaging. Moreover, Differential SAR Tomography (D-TomoSAR) extends the synthetic aperture principle into the elevation and time directions simultaneously. Thus, it can obtain the target 3-D scattering structure along with the deformation speed of the observed target. GaoFen-3 (GF-3) is the first C-band multi-polarization 1 m resolution SAR satellite of China. It has several advantages, such as high-resolution, large swath width, and multiple imaging modes, which are crucial to the development of a high-resolution earth observation technology for China. Presently, GF-3 data are mainly used in the image processing field, such as target identification. However, the phase information of the SAR images is not yet fully utilized. Moreover, because of the high-dimensional imaging ability that was overlooked at the beginning of designing the system, existing SAR images acquired by GF-3 have spatial and temporal de-coherence problems. Thus, it is difficult to use the images in further interference series processing. To solve the above problems, this study achieved 3-D and four-Dimensional (4-D) imaging of buildings around Yanqi Lake, in Beijing, based on the data of seven SAR complex images. We obtained the 3-D scattering structure information of buildings and achieved millimeter-level high-precision monitoring of building deformation. The preliminary experimental results demonstrate the application potential of GF-3 SAR data and provide a technical support for the subsequent further application of the GF-3 SAR satellite in urban sensing and monitoring.
Conventional Synthetic Aperture Radar (SAR) can only obtain two-dimensional (2-D) azimuth-range images without accurately reflecting the three-Dimensional (3-D) scattering structure information of the targets. However, SAR Tomography (TomoSAR) is a multi-baseline interferometric measurement mode that extends the synthetic aperture principle into the elevation direction, making it possible to recover the true height of the target, thereby achieving 3-D imaging. Moreover, Differential SAR Tomography (D-TomoSAR) extends the synthetic aperture principle into the elevation and time directions simultaneously. Thus, it can obtain the target 3-D scattering structure along with the deformation speed of the observed target. GaoFen-3 (GF-3) is the first C-band multi-polarization 1 m resolution SAR satellite of China. It has several advantages, such as high-resolution, large swath width, and multiple imaging modes, which are crucial to the development of a high-resolution earth observation technology for China. Presently, GF-3 data are mainly used in the image processing field, such as target identification. However, the phase information of the SAR images is not yet fully utilized. Moreover, because of the high-dimensional imaging ability that was overlooked at the beginning of designing the system, existing SAR images acquired by GF-3 have spatial and temporal de-coherence problems. Thus, it is difficult to use the images in further interference series processing. To solve the above problems, this study achieved 3-D and four-Dimensional (4-D) imaging of buildings around Yanqi Lake, in Beijing, based on the data of seven SAR complex images. We obtained the 3-D scattering structure information of buildings and achieved millimeter-level high-precision monitoring of building deformation. The preliminary experimental results demonstrate the application potential of GF-3 SAR data and provide a technical support for the subsequent further application of the GF-3 SAR satellite in urban sensing and monitoring.
10
2022, 11(4): 581-599.
This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR .
This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR .
11
2022, 11(5): 830-849.
Due to the range dependence and time-varying array factor of Frequency Diverse Array (FDA) radar, it can overcome the miss of range variable in traditional phased-array factor and gain loss of Multiple-Input Multiple-Output (MIMO) radar array. In recent years, FDA radar techniques have attracted more and more attention of researches and institutions. Nevertheless, there are still many open problems to be solved in FDA radar system theory, signal processing and application implementation. In this overviewing paper, we introduced the FDA concepts, motivation and extending techniques. The latest research advances on FDA radars and their applications are comprehensively reviewed, and the typical application prospects of FDA in jamming radar and radar anti-jamming, ambiguous clutter suppression and blind velocity target detection together with localization deception are discussed. Finally, several key research problems that need to be solved in future work are pointed out.
Due to the range dependence and time-varying array factor of Frequency Diverse Array (FDA) radar, it can overcome the miss of range variable in traditional phased-array factor and gain loss of Multiple-Input Multiple-Output (MIMO) radar array. In recent years, FDA radar techniques have attracted more and more attention of researches and institutions. Nevertheless, there are still many open problems to be solved in FDA radar system theory, signal processing and application implementation. In this overviewing paper, we introduced the FDA concepts, motivation and extending techniques. The latest research advances on FDA radars and their applications are comprehensively reviewed, and the typical application prospects of FDA in jamming radar and radar anti-jamming, ambiguous clutter suppression and blind velocity target detection together with localization deception are discussed. Finally, several key research problems that need to be solved in future work are pointed out.
12
2022, 11(2): 278-288.
As a typical coherent jamming method, Interrupted Sampling Repeater Jamming (ISRJ) can generate multiple false targets with high fidelity at the radar receiver using under-sampling, which causes real targets detection to become invalid. To solve this problem, an anti-ISRJ method based on the joint design of Doppler-tolerant complementary sequences and receiving filters is proposed in this paper. First, by considering the Doppler tolerance of sequences, the sum of the energy of the ambiguity function of transmitted sequences and receiving filters and energy of the ambiguity function of ISRJ signals and receiving filters is chosen as the objective function. Meanwhile, the constant modulus constraint on sequences and signal-to-noise ratio constraint are considered. Then, an alternately iterative algorithm based on the Majorization-Minimization (MM) method is proposed to solve the non-convex optimization problem. Finally, numerical results are presented as a way to compare with conventional methods, and the sequences and receiving filters designed by the proposed method show better pulse compression correlation and anti-ISRJ performance. These procedures can substantially improve the radar detection ability to move targets in the jamming scene.
As a typical coherent jamming method, Interrupted Sampling Repeater Jamming (ISRJ) can generate multiple false targets with high fidelity at the radar receiver using under-sampling, which causes real targets detection to become invalid. To solve this problem, an anti-ISRJ method based on the joint design of Doppler-tolerant complementary sequences and receiving filters is proposed in this paper. First, by considering the Doppler tolerance of sequences, the sum of the energy of the ambiguity function of transmitted sequences and receiving filters and energy of the ambiguity function of ISRJ signals and receiving filters is chosen as the objective function. Meanwhile, the constant modulus constraint on sequences and signal-to-noise ratio constraint are considered. Then, an alternately iterative algorithm based on the Majorization-Minimization (MM) method is proposed to solve the non-convex optimization problem. Finally, numerical results are presented as a way to compare with conventional methods, and the sequences and receiving filters designed by the proposed method show better pulse compression correlation and anti-ISRJ performance. These procedures can substantially improve the radar detection ability to move targets in the jamming scene.
13
2022, 11(1): 71-82.
The tomographic technique has attracted much attention because of its ability to separate overlapping scatterers in urban Synthetic Aperture Radar (SAR) images. The general method of SAR Tomography (TomSAR) imaging combines the following two aspects: estimating the distribution of the scatterers in the elevation direction and determining the number of strong scatterers in an overlapped pixel. This study applied several sophisticated spectrum estimations (e.g., Orthogonal Matching Pursuit, Sparse Learning via Iterative Minimization and Multiple Signal Classification) and model order selection approaches (e.g., Bayesian information criterion and generalized likelihood ratio test) with highly technical potential to recover the simulated overlapping scatterers. This simulation experiment is based on the parameters of the AIRCAS X-band TomoSAR data from Emei, Sichuan, China. The Cramér-Rao Lower Bound (CRLB) and recovery probability are used to evaluate the performances of different methods for the separation of overlapped scatterers. The experimental results revealed the following: (1) the standard deviation of estimation using second-order statistics is smaller than that of a single observation vector, especially when the number of acquisitions is very limited; (2) the amplitude ratio, phase difference, and elevation spacing between overlapping scatterers will have a significant impact on the different kinds of algorithms; and (3) the phase difference between overlapping scatterers will make the phase center estimation of greedy algorithm or spectrum estimation algorithm biased.
The tomographic technique has attracted much attention because of its ability to separate overlapping scatterers in urban Synthetic Aperture Radar (SAR) images. The general method of SAR Tomography (TomSAR) imaging combines the following two aspects: estimating the distribution of the scatterers in the elevation direction and determining the number of strong scatterers in an overlapped pixel. This study applied several sophisticated spectrum estimations (e.g., Orthogonal Matching Pursuit, Sparse Learning via Iterative Minimization and Multiple Signal Classification) and model order selection approaches (e.g., Bayesian information criterion and generalized likelihood ratio test) with highly technical potential to recover the simulated overlapping scatterers. This simulation experiment is based on the parameters of the AIRCAS X-band TomoSAR data from Emei, Sichuan, China. The Cramér-Rao Lower Bound (CRLB) and recovery probability are used to evaluate the performances of different methods for the separation of overlapped scatterers. The experimental results revealed the following: (1) the standard deviation of estimation using second-order statistics is smaller than that of a single observation vector, especially when the number of acquisitions is very limited; (2) the amplitude ratio, phase difference, and elevation spacing between overlapping scatterers will have a significant impact on the different kinds of algorithms; and (3) the phase difference between overlapping scatterers will make the phase center estimation of greedy algorithm or spectrum estimation algorithm biased.
14
2022, 11(2): 213-226.
The pseudo-random agility technology for interpulse waveform parameters in airborne radar increases the complexity and uncertainty of radar waveform and improves its anti-clutter and anti-interference ability by optimizing the pulse repetition interval, initial phase, frequency, and amplitude, which is one of the main developmental directions of airborne radar technology. The pseudo-random agility of interpulse parameters makes multi-pulse coherent accumulation and modeling of clutter spectrum characteristics difficult. In this paper, a pseudo-random agility signal model of interpulse parameters is established. Furthermore, a non-uniform parameter coherent processing method is proposed, and the anti-interference performance is analyzed. Based on the analysis, the clutter echo model of airborne radar with random pulse repetition interval is studied, and a joint transmitter-receiver filter design is proposed for strong clutter processing. Finally, numerical simulation is conducted to verify the results.
The pseudo-random agility technology for interpulse waveform parameters in airborne radar increases the complexity and uncertainty of radar waveform and improves its anti-clutter and anti-interference ability by optimizing the pulse repetition interval, initial phase, frequency, and amplitude, which is one of the main developmental directions of airborne radar technology. The pseudo-random agility of interpulse parameters makes multi-pulse coherent accumulation and modeling of clutter spectrum characteristics difficult. In this paper, a pseudo-random agility signal model of interpulse parameters is established. Furthermore, a non-uniform parameter coherent processing method is proposed, and the anti-interference performance is analyzed. Based on the analysis, the clutter echo model of airborne radar with random pulse repetition interval is studied, and a joint transmitter-receiver filter design is proposed for strong clutter processing. Finally, numerical simulation is conducted to verify the results.
15
2022, 11(3): 469-478.
Conventional multitarget-tracking data association algorithms must have prior information, such as the target motion model and clutter density. However, such prior information cannot be obtained timely and accurately before tracking. To address this issue, a data association algorithm for multitarget tracking based on a transformer network is proposed. First, considering that the radar may not perform accurate detected the target, virtual measurements are performed to re-establish the data association model. Thus, a data association method based on the transformer network is proposed to solve the matching problem of multitargets and multimeasurements. Moreover, a loss function combining Masked Cross entropy loss and Dice (MCD) loss is designed to optimize the network parameters. Simulation data and real measurement data results show that the proposed algorithm outperforms classic data association algorithms and algorithms based on bidirectional long short-term memory network under varying detection probability conditions.
Conventional multitarget-tracking data association algorithms must have prior information, such as the target motion model and clutter density. However, such prior information cannot be obtained timely and accurately before tracking. To address this issue, a data association algorithm for multitarget tracking based on a transformer network is proposed. First, considering that the radar may not perform accurate detected the target, virtual measurements are performed to re-establish the data association model. Thus, a data association method based on the transformer network is proposed to solve the matching problem of multitargets and multimeasurements. Moreover, a loss function combining Masked Cross entropy loss and Dice (MCD) loss is designed to optimize the network parameters. Simulation data and real measurement data results show that the proposed algorithm outperforms classic data association algorithms and algorithms based on bidirectional long short-term memory network under varying detection probability conditions.
16
2022, 11(6): 921-942.
One remarkable trend in applying synthetic aperture radar technology is the automatic interpretation of Synthetic Aperture Radar (SAR) images. The electromagnetic scattering characteristics have a robust correlation with the target structure, which provides key support for SAR image interpretation. Therefore, elucidating how to extract accurate electromagnetic characteristics and how to use these electromagnetic characteristics to retrieve target characteristics has been widely valued recently. This study discusses the research accomplishments, summarizes the key elements and ideas of electromagnetic characteristic extraction and electromagnetic-characteristic-based target recognition, and details the extension applications of the electromagnetic scattering mechanism in imaging and recognition. Finally, the future research direction of electromagnetic scattering characteristic extraction and application was proposed.
One remarkable trend in applying synthetic aperture radar technology is the automatic interpretation of Synthetic Aperture Radar (SAR) images. The electromagnetic scattering characteristics have a robust correlation with the target structure, which provides key support for SAR image interpretation. Therefore, elucidating how to extract accurate electromagnetic characteristics and how to use these electromagnetic characteristics to retrieve target characteristics has been widely valued recently. This study discusses the research accomplishments, summarizes the key elements and ideas of electromagnetic characteristic extraction and electromagnetic-characteristic-based target recognition, and details the extension applications of the electromagnetic scattering mechanism in imaging and recognition. Finally, the future research direction of electromagnetic scattering characteristic extraction and application was proposed.
17
2022, 11(1): 27-39.
A radar human behavior perception system has penetration detection ability, which gives it a wide application prospect in the fields of security, rescue, medical treatment, and so on. Although the development of deep learning technology has promoted radar sensor research in human behavior perception, it requires more prompted dataset availability. This paper provides a four-dimensional imaging dataset of human activity using ultra-wideband radar, UWB-HA4D, which uses three-dimensional ultra-wideband multiple-input multiple-output radar as the detection sensor to capture the range-azimuth-height-time four-dimensional activity data of a human target. The dataset contains the activity data of 2757 groups for 11 human targets, including 10 common activities, such as walking, waving, and boxing. It also contains penetration and nonpenetration detection experimental scenarios. The radar system parameters, data generation process, data distribution, and other information of the dataset are introduced in detail herein. Meanwhile, several deep learning algorithms that are based on the PaddlePaddle framework and are widely used in the computer version field are applied to this dataset for human activity recognition. The experimental comparison results can be used to provide references for scholars and facilitate further investigation and research on this basis.
A radar human behavior perception system has penetration detection ability, which gives it a wide application prospect in the fields of security, rescue, medical treatment, and so on. Although the development of deep learning technology has promoted radar sensor research in human behavior perception, it requires more prompted dataset availability. This paper provides a four-dimensional imaging dataset of human activity using ultra-wideband radar, UWB-HA4D, which uses three-dimensional ultra-wideband multiple-input multiple-output radar as the detection sensor to capture the range-azimuth-height-time four-dimensional activity data of a human target. The dataset contains the activity data of 2757 groups for 11 human targets, including 10 common activities, such as walking, waving, and boxing. It also contains penetration and nonpenetration detection experimental scenarios. The radar system parameters, data generation process, data distribution, and other information of the dataset are introduced in detail herein. Meanwhile, several deep learning algorithms that are based on the PaddlePaddle framework and are widely used in the computer version field are applied to this dataset for human activity recognition. The experimental comparison results can be used to provide references for scholars and facilitate further investigation and research on this basis.
18
2022, 11(3): 335-346.
The anchor-free network represented by a Fully Convolutional One-Stage object detector (FCOS) avoids the hyperparameter setting issue caused by the preset anchor boxes; however, the result of the horizontal bounding boxes cannot indicate the precise boundary and orientation of the arbitrary-oriented ship detection in synthetic-aperture radar images. To solve this problem, this paper proposes a detection algorithm named FCOSR. First, the angle parameter is added to the FCOS regression branch to output the rotatable bounding boxes. Second, 9-point features based on deformable convolution are introduced to predict the ship confidence and the boundary-box residual to reduce the land false alarm and improve the accuracy of the boundary box regression. Finally, in the training stage, the rotatable adaptive sample selection strategy is used to allocate appropriate positive sample points to the real ship to improve the network detection accuracy. Compared to the FCOS and currently published anchor-based rotatable detection networks, the proposed network exhibited faster detection speed and higher detection accuracy on the SSDD+ and HRSID datasets with the mAPs of 91.7% and 84.3%, respectively. The average detection time of image slices was only 33 ms.
The anchor-free network represented by a Fully Convolutional One-Stage object detector (FCOS) avoids the hyperparameter setting issue caused by the preset anchor boxes; however, the result of the horizontal bounding boxes cannot indicate the precise boundary and orientation of the arbitrary-oriented ship detection in synthetic-aperture radar images. To solve this problem, this paper proposes a detection algorithm named FCOSR. First, the angle parameter is added to the FCOS regression branch to output the rotatable bounding boxes. Second, 9-point features based on deformable convolution are introduced to predict the ship confidence and the boundary-box residual to reduce the land false alarm and improve the accuracy of the boundary box regression. Finally, in the training stage, the rotatable adaptive sample selection strategy is used to allocate appropriate positive sample points to the real ship to improve the network detection accuracy. Compared to the FCOS and currently published anchor-based rotatable detection networks, the proposed network exhibited faster detection speed and higher detection accuracy on the SSDD+ and HRSID datasets with the mAPs of 91.7% and 84.3%, respectively. The average detection time of image slices was only 33 ms.
19
2022, 11(4): 570-580.
To prevent the degradation of the detection performance of Dual-Function Radar-Communication (DFRC) system in the presence of clutter, we propose the joint design of a transmit waveform and receiver filter to suppress the clutter and enhance the target detection performance. We use the Signal-to-Interference-plus-Noise Ratio (SINR) as the design criterion. Meanwhile, the Multi-User Interference (MUI) energy of the communication signals is constrained to maintain the quality of service for information transmission via DFRC systems. In addition, a similarity constraint is enforced to enable the transmitted waveform to have a good ambiguity function. To tackle the joint optimization problem, we present an iterative algorithm based on cyclic optimization and Semi-Definite Relaxation (SDR). The convergence of the algorithm is proved by a theoretical analysis. The simulation results show that the designed waveform can improve the target detection performance of a DFRC system in clutter and efficiently realize multi-user communication.
To prevent the degradation of the detection performance of Dual-Function Radar-Communication (DFRC) system in the presence of clutter, we propose the joint design of a transmit waveform and receiver filter to suppress the clutter and enhance the target detection performance. We use the Signal-to-Interference-plus-Noise Ratio (SINR) as the design criterion. Meanwhile, the Multi-User Interference (MUI) energy of the communication signals is constrained to maintain the quality of service for information transmission via DFRC systems. In addition, a similarity constraint is enforced to enable the transmitted waveform to have a good ambiguity function. To tackle the joint optimization problem, we present an iterative algorithm based on cyclic optimization and Semi-Definite Relaxation (SDR). The convergence of the algorithm is proved by a theoretical analysis. The simulation results show that the designed waveform can improve the target detection performance of a DFRC system in clutter and efficiently realize multi-user communication.
20
2022, 11(5): 897-908.
Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations.
Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations.
- First
- Prev
- 1
- 2
- 3
- 4
- Next
- Last
- Total:4
- To
- Go
微信 | 公众平台 
