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Compared with single-radar systems, spatially separated networked radar usually has better detection performance due to its advantages of spatial and frequency diversities. Most of the current fusion detection methods based on networked radar systems only rely on the echo amplitude information of the target without considering the Doppler information that a coherent radar system can obtain to assist detection of targets. Intuitively, the spatial position and radial velocity of a target observed by different radar devices in the networked radar systems should meet certain physical constraints under which the target and false target can be substantially distinguished. Based on this consideration, fusion detection for the networked radar aided by a Doppler information algorithm is proposed in this paper. First, a set of inequalities is constructed based on the coupling between the observation of the same target’s azimuth and Doppler velocity by multiple radar stations. Then, a two-phase method, an algorithm in operational research, is used to judge whether the inequalities have a feasible solution, based on which a judgment is made on whether the target exists. Finally, some simulations are conducted, which show that the proposed algorithm can effectively improve the detection performance of the networked radar system fusion detection. Additionally, the influence of radar station location and target position on the fusion detection performance of the proposed algorithm is analyzed.
Compared with single-radar systems, spatially separated networked radar usually has better detection performance due to its advantages of spatial and frequency diversities. Most of the current fusion detection methods based on networked radar systems only rely on the echo amplitude information of the target without considering the Doppler information that a coherent radar system can obtain to assist detection of targets. Intuitively, the spatial position and radial velocity of a target observed by different radar devices in the networked radar systems should meet certain physical constraints under which the target and false target can be substantially distinguished. Based on this consideration, fusion detection for the networked radar aided by a Doppler information algorithm is proposed in this paper. First, a set of inequalities is constructed based on the coupling between the observation of the same target’s azimuth and Doppler velocity by multiple radar stations. Then, a two-phase method, an algorithm in operational research, is used to judge whether the inequalities have a feasible solution, based on which a judgment is made on whether the target exists. Finally, some simulations are conducted, which show that the proposed algorithm can effectively improve the detection performance of the networked radar system fusion detection. Additionally, the influence of radar station location and target position on the fusion detection performance of the proposed algorithm is analyzed.
With the substantial improvement of Synthetic Aperture Radar (SAR) regarding swath width and spatial and temporal resolutions, a time series obtained by registering SAR images acquired at different times can provide more accurate information on the dynamic changes in the observed areas. However, inherent speckle noise and outliers along the temporal dimension in the time series pose serious challenges for subsequent interpretation tasks. Although existing state-of-the-art methods can effectively suppress the speckle noise in a SAR time series, outliers along the temporal dimension will interfere with the denoising results. To better solve this problem, this paper proposes an additive signal decomposition method in the logarithm domain that can suppress the speckle noise and separate stable data and outliers along the temporal dimension in a time series, thus eliminating the impact of outliers on the denoising results. When the simulated data are disturbed by outliers, the proposed method can achieve an approximately 3 dB improvement in the Peak Signal-to-Noise Ratio (PSNR) compared to the other state-of-the-art methods. On Sentinel-1 data, the proposed method robustly suppresses the speckle noise in a time series, and the obtained outliers along the temporal dimension provide reference data for subsequent interpretation tasks.
With the substantial improvement of Synthetic Aperture Radar (SAR) regarding swath width and spatial and temporal resolutions, a time series obtained by registering SAR images acquired at different times can provide more accurate information on the dynamic changes in the observed areas. However, inherent speckle noise and outliers along the temporal dimension in the time series pose serious challenges for subsequent interpretation tasks. Although existing state-of-the-art methods can effectively suppress the speckle noise in a SAR time series, outliers along the temporal dimension will interfere with the denoising results. To better solve this problem, this paper proposes an additive signal decomposition method in the logarithm domain that can suppress the speckle noise and separate stable data and outliers along the temporal dimension in a time series, thus eliminating the impact of outliers on the denoising results. When the simulated data are disturbed by outliers, the proposed method can achieve an approximately 3 dB improvement in the Peak Signal-to-Noise Ratio (PSNR) compared to the other state-of-the-art methods. On Sentinel-1 data, the proposed method robustly suppresses the speckle noise in a time series, and the obtained outliers along the temporal dimension provide reference data for subsequent interpretation tasks.
In radar-based road target recognition, the increase in target feature dimension is a common technique to improve recognition performance when targets become diverse, but their characteristics are similar. However, the increase in feature dimension leads to feature redundancy and dimension disasters. Therefore, it is necessary to optimize the extracted high-dimensional feature set. The Adaptive Genetic Algorithm (AGA) based on random search is an effective feature optimization method. To improve the efficiency and accuracy of the AGA, the existing improved AGA methods generally utilize the prior correlation between features and targets for pre-dimensionality reduction of high-dimensional feature sets. However, such algorithms only consider the correlation between a single feature and a target, neglecting the correlation between feature combinations and targets. The selected feature set may not be the best recognition combination for the target. Thus, to address this issue, this study proposes an improved AGA via pre-dimensionality reduction based on Histogram Analysis (HA) of the correlation between different feature combinations and targets. The proposed method can simultaneously improve the efficiency and accuracy of feature selection and target recognition performance. Comparative experiments based on a real dataset of the millimeter-wave radar showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 95.7%, which is 1.9%, 2.4%, and 10.1% higher than that of IG-GA, ReliefF-IAGA, and improved RetinaNet methods, respectively. Comparative experiments based on the CARRADA dataset showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 93.0%, which is 1.2% and 1.5% higher than that of IG-GA and ReliefF-IAGA methods, respectively. These results verify the effectiveness and superiority of the proposed method compared with existing methods. In addition, the performance of different feature optimization methods coupled with the integrated bagging tree, fine tree, and K-Nearest Neighbor (KNN) classifier was compared. The experimental results showed that the proposed method exhibits evident advantages when coupled with different classifiers and has broad applicability.
In radar-based road target recognition, the increase in target feature dimension is a common technique to improve recognition performance when targets become diverse, but their characteristics are similar. However, the increase in feature dimension leads to feature redundancy and dimension disasters. Therefore, it is necessary to optimize the extracted high-dimensional feature set. The Adaptive Genetic Algorithm (AGA) based on random search is an effective feature optimization method. To improve the efficiency and accuracy of the AGA, the existing improved AGA methods generally utilize the prior correlation between features and targets for pre-dimensionality reduction of high-dimensional feature sets. However, such algorithms only consider the correlation between a single feature and a target, neglecting the correlation between feature combinations and targets. The selected feature set may not be the best recognition combination for the target. Thus, to address this issue, this study proposes an improved AGA via pre-dimensionality reduction based on Histogram Analysis (HA) of the correlation between different feature combinations and targets. The proposed method can simultaneously improve the efficiency and accuracy of feature selection and target recognition performance. Comparative experiments based on a real dataset of the millimeter-wave radar showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 95.7%, which is 1.9%, 2.4%, and 10.1% higher than that of IG-GA, ReliefF-IAGA, and improved RetinaNet methods, respectively. Comparative experiments based on the CARRADA dataset showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 93.0%, which is 1.2% and 1.5% higher than that of IG-GA and ReliefF-IAGA methods, respectively. These results verify the effectiveness and superiority of the proposed method compared with existing methods. In addition, the performance of different feature optimization methods coupled with the integrated bagging tree, fine tree, and K-Nearest Neighbor (KNN) classifier was compared. The experimental results showed that the proposed method exhibits evident advantages when coupled with different classifiers and has broad applicability.
As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset.
As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset.
Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.
Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.
For the Multi-Target Tracking (MTT) of distributed netted phased array radars, this paper proposes a joint beam and dwell time allocation algorithm driven by dynamic threats. First, a Bayesian Cramer-Rao Lower Bound (BCRLB), including beam and dwell time allocation, is derived. Then, a comprehensive threat evaluation scale is constructed based on the real-time motion state of the target, and a utility function based on the tracking accuracy reference threshold and contributed weights is designed for targets with different threats to measure the relationship of resource allocation prioritization among multiple targets. Afterward, an optimal distribution model of the joint beam and the dwell time driven by the dynamic threat of the target is established; the utility function is combined with the resources of the netted phased array radar system. Finally, the problem is solved using a reward-based iterative descent search algorithm, and the effectiveness of the algorithm is verified via simulation. The simulation results show that the proposed algorithm can determine the tracking accuracy requirements of different targets and allocate tracking resources based on the multi-target threat assessment results, thereby improving the comprehensive tracking accuracy of networked phased array radars.
For the Multi-Target Tracking (MTT) of distributed netted phased array radars, this paper proposes a joint beam and dwell time allocation algorithm driven by dynamic threats. First, a Bayesian Cramer-Rao Lower Bound (BCRLB), including beam and dwell time allocation, is derived. Then, a comprehensive threat evaluation scale is constructed based on the real-time motion state of the target, and a utility function based on the tracking accuracy reference threshold and contributed weights is designed for targets with different threats to measure the relationship of resource allocation prioritization among multiple targets. Afterward, an optimal distribution model of the joint beam and the dwell time driven by the dynamic threat of the target is established; the utility function is combined with the resources of the netted phased array radar system. Finally, the problem is solved using a reward-based iterative descent search algorithm, and the effectiveness of the algorithm is verified via simulation. The simulation results show that the proposed algorithm can determine the tracking accuracy requirements of different targets and allocate tracking resources based on the multi-target threat assessment results, thereby improving the comprehensive tracking accuracy of networked phased array radars.
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2023, 12(1): 1-12.
The theoretical azimuth resolution of Synthetic Aperture Radar (SAR) is half the antenna length, presenting conflicting requirements on the antenna for SAR high-resolution and long-distance imaging. In this paper, a method for antenna array coding synthetic aperture imaging is proposed. By dividing long antennas into subarrays to work together, the space energy utilization rate can be improved, a high resolution of the small antennas of the subarrays and a high gain of the long antennas of the whole array are achieved, and the problem that it is difficult to balance high-resolution and far-distance imaging is solved. On the basis of introducing the basic concept of array coding, the imaging model and processing flow of array coding radar are given, and the system performance metrics, such as resolution, signal-to-noise ratio, Pulse Repetition Frequency (PRF), and range-azimuth ambiguity, are theoretically analyzed and discussed. In a flight test experiment, a 0.5 m antenna is divided into four sub-arrays, and a strip-map image with a resolution of 0.1 m and a swath width of 8 km is obtained, which breaks the restriction of small-area imaging when traditional SAR adopts the spotlight mode to achieve high resolution. This new method effectively solves the limitations of traditional SAR and extends the signal dimension, providing a technical basis for enhancing radar system capability. Theoretical analysis and experimental results verify the considerable advantages and engineering implementation feasibility of the proposed method.
The theoretical azimuth resolution of Synthetic Aperture Radar (SAR) is half the antenna length, presenting conflicting requirements on the antenna for SAR high-resolution and long-distance imaging. In this paper, a method for antenna array coding synthetic aperture imaging is proposed. By dividing long antennas into subarrays to work together, the space energy utilization rate can be improved, a high resolution of the small antennas of the subarrays and a high gain of the long antennas of the whole array are achieved, and the problem that it is difficult to balance high-resolution and far-distance imaging is solved. On the basis of introducing the basic concept of array coding, the imaging model and processing flow of array coding radar are given, and the system performance metrics, such as resolution, signal-to-noise ratio, Pulse Repetition Frequency (PRF), and range-azimuth ambiguity, are theoretically analyzed and discussed. In a flight test experiment, a 0.5 m antenna is divided into four sub-arrays, and a strip-map image with a resolution of 0.1 m and a swath width of 8 km is obtained, which breaks the restriction of small-area imaging when traditional SAR adopts the spotlight mode to achieve high resolution. This new method effectively solves the limitations of traditional SAR and extends the signal dimension, providing a technical basis for enhancing radar system capability. Theoretical analysis and experimental results verify the considerable advantages and engineering implementation feasibility of the proposed method.
2023, 12(1): 13-35.
Bi/multistatic Synthetic Aperture Radar (SAR) using spaceborne illuminator utilizes spaceborne platforms as transmitters and satellites, near-space vehicles, aircraft, and ground platforms as receivers, which allows high-resolution imaging of ground and marine scenes and targets. The system’s benefits include a broad imaging field of view, high concealment, and potent antijamming abilities. By using beam steering methods, a variety of imaging modes can be achieved, such as staring spotlight and sliding spotlight modes, which obtain abundant information about the imaging scene and offer broad application prospects both in civil and military fields. Thus far, the bi/multistatic SAR using spaceborne illuminator has been intensively investigated, and several research findings have been reported. This paper examines the technology from the aspects of system components, configuration design, echo model, imaging techniques, bistatic synchronization, and experimental verification and systematically reviews the state-of-the-art research progress. Finally, it is anticipated that spaceborne illuminator technology will be used to develop bi/multistatic SAR in the future.
Bi/multistatic Synthetic Aperture Radar (SAR) using spaceborne illuminator utilizes spaceborne platforms as transmitters and satellites, near-space vehicles, aircraft, and ground platforms as receivers, which allows high-resolution imaging of ground and marine scenes and targets. The system’s benefits include a broad imaging field of view, high concealment, and potent antijamming abilities. By using beam steering methods, a variety of imaging modes can be achieved, such as staring spotlight and sliding spotlight modes, which obtain abundant information about the imaging scene and offer broad application prospects both in civil and military fields. Thus far, the bi/multistatic SAR using spaceborne illuminator has been intensively investigated, and several research findings have been reported. This paper examines the technology from the aspects of system components, configuration design, echo model, imaging techniques, bistatic synchronization, and experimental verification and systematically reviews the state-of-the-art research progress. Finally, it is anticipated that spaceborne illuminator technology will be used to develop bi/multistatic SAR in the future.
2023, 12(1): 36-52.
The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm’s imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.
The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm’s imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.
2023, 12(1): 53-63.
Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide.
Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide.
2023, 12(1): 64-75.
To refine the geometric parameters of satellite-based SAR images and improve the stereo positioning accuracy, a method of SAR elevation control point extraction combining multistrategy ATLAS data preference and image matching has been developed. This method is based on the concept of optical remote sensing image elevation control point extraction from satellite-based laser altimetry data. The method employs various strategies, such as non-night observation photon filtering, high confidence photon selection, SRTM DEM-assisted coarse difference rejection, and large eccentricity elliptical filtering kernel flat area photon screening. To extract laser elevation points with high quality and flat area from ATLAS data for ATL03 level products. Then the geocoding of the slant range SAR images is performed using the SRTM DEM. The local Google Earth images are selected as the footprint images according to the plane coordinates of the laser elevation points. The rank self-similarity descriptor is used to match the footprint images with the SAR geocoded images. The coordinates of the SAR images corresponding to the laser elevation points are obtained. Thus, SAR elevation control points are extracted. The extraction of GF-3 SAR elevation control points was performed using ATLAS data from two regions: Dengfeng, China, and Yokosuka, Japan. The geometric parameter refinement of SAR images using extracted elevation control points significantly improved the accuracy of stereo positioning and verified that the method for extracting elevation control points described in this paper is feasible and effective.
To refine the geometric parameters of satellite-based SAR images and improve the stereo positioning accuracy, a method of SAR elevation control point extraction combining multistrategy ATLAS data preference and image matching has been developed. This method is based on the concept of optical remote sensing image elevation control point extraction from satellite-based laser altimetry data. The method employs various strategies, such as non-night observation photon filtering, high confidence photon selection, SRTM DEM-assisted coarse difference rejection, and large eccentricity elliptical filtering kernel flat area photon screening. To extract laser elevation points with high quality and flat area from ATLAS data for ATL03 level products. Then the geocoding of the slant range SAR images is performed using the SRTM DEM. The local Google Earth images are selected as the footprint images according to the plane coordinates of the laser elevation points. The rank self-similarity descriptor is used to match the footprint images with the SAR geocoded images. The coordinates of the SAR images corresponding to the laser elevation points are obtained. Thus, SAR elevation control points are extracted. The extraction of GF-3 SAR elevation control points was performed using ATLAS data from two regions: Dengfeng, China, and Yokosuka, Japan. The geometric parameter refinement of SAR images using extracted elevation control points significantly improved the accuracy of stereo positioning and verified that the method for extracting elevation control points described in this paper is feasible and effective.
2023, 12(1): 76-109.
The ocean surface is a complicated dynamic system with considerable irregularity and nonrepetition in space and time. Sea clutter is the superposition of a large number of scatterer echoes generated by the radar electromagnetic signal irradiated to the sea surface, which is affected by wind, currents, waves, etc. and shows nonuniformity and nonsmoothness. The sea clutter signal has a certain interference effect on the detection of sea targets, especially under high sea conditions when the waves are furious, and the target signal is readily drowned out by the strong sea clutter signal, severely limiting the radar’s detection capability on sea targets. The investigation of sea clutter and target electromagnetic scattering properties serves as the foundation for improving the target detection capability in difficult marine environments. The formation of target echo data in the actual marine environment is of great significance for the analysis of sea clutter and target radar echo characteristics, as well as the supplementation of the actual measurement data set based on electromagnetic waves and the actual complex dynamic sea surface and target electromagnetic scattering mechanism. This study summarizes three key categories of echo simulation methods, analyzes the benefits, disadvantages, and adaptability of several categories of methods for the characteristics of the sea surface and target simulation scenarios, and provides some simulation results in order to make recent advancements and future trends of physics-based complex sea environment and target echo simulation methods more accessible to relevant researchers. It also introduces some echo datasets based on real measurements, which can facilitate scholars’ analysis of echo characteristics. Lastly, the trend toward developing complex sea surface and target echo simulation methods and characteristics for research is presented.
The ocean surface is a complicated dynamic system with considerable irregularity and nonrepetition in space and time. Sea clutter is the superposition of a large number of scatterer echoes generated by the radar electromagnetic signal irradiated to the sea surface, which is affected by wind, currents, waves, etc. and shows nonuniformity and nonsmoothness. The sea clutter signal has a certain interference effect on the detection of sea targets, especially under high sea conditions when the waves are furious, and the target signal is readily drowned out by the strong sea clutter signal, severely limiting the radar’s detection capability on sea targets. The investigation of sea clutter and target electromagnetic scattering properties serves as the foundation for improving the target detection capability in difficult marine environments. The formation of target echo data in the actual marine environment is of great significance for the analysis of sea clutter and target radar echo characteristics, as well as the supplementation of the actual measurement data set based on electromagnetic waves and the actual complex dynamic sea surface and target electromagnetic scattering mechanism. This study summarizes three key categories of echo simulation methods, analyzes the benefits, disadvantages, and adaptability of several categories of methods for the characteristics of the sea surface and target simulation scenarios, and provides some simulation results in order to make recent advancements and future trends of physics-based complex sea environment and target echo simulation methods more accessible to relevant researchers. It also introduces some echo datasets based on real measurements, which can facilitate scholars’ analysis of echo characteristics. Lastly, the trend toward developing complex sea surface and target echo simulation methods and characteristics for research is presented.
2023, 12(1): 110-119.
Using Deep Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics.
Using Deep Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics.
2023, 12(1): 120-139.
Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.
Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.
2023, 12(1): 140-153.
In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications.
In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications.
2023, 12(1): 154-172.
Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric interference suppression methods, which heavily rely on interference priori and strong energy difference, have serious problems such as high computational complexity and signal loss, and have difficulty in meeting the needs of countering increasingly complex interference. To solve the aforementioned problems, we propose an anti-interference method using self-supervised learning based on deep learning, which uses the time-frequency domain texture difference between normal radar echo and interference to overcome the constraint of using interference prior. First, we construct an interference location network model Location-Net, which compresses and reconstructs the time-frequency spectrum of the radar echo and locates the interference according to the network’s reconstruction error. Second, aiming at the signal loss caused by interference suppression, a signal recovery neural network model Recovery-Net is constructed to recover the echo signal after interference suppression. Compared with traditional methods, our method overcomes the need for interference prior, can effectively resist various complex interference types and has strong generalization ability. The anti-interference processing results based on simulation and measured data verify the effectiveness of the proposed method for various active main lobe suppression interference and show the superiority of the algorithm proposed here by comparing it with three existing anti-interference methods. Finally, comparing the complexity difference between the proposed and mainstream lightweight neural networks shows that the neural networks designed here have low computational complexity and real-time application prospects.
Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric interference suppression methods, which heavily rely on interference priori and strong energy difference, have serious problems such as high computational complexity and signal loss, and have difficulty in meeting the needs of countering increasingly complex interference. To solve the aforementioned problems, we propose an anti-interference method using self-supervised learning based on deep learning, which uses the time-frequency domain texture difference between normal radar echo and interference to overcome the constraint of using interference prior. First, we construct an interference location network model Location-Net, which compresses and reconstructs the time-frequency spectrum of the radar echo and locates the interference according to the network’s reconstruction error. Second, aiming at the signal loss caused by interference suppression, a signal recovery neural network model Recovery-Net is constructed to recover the echo signal after interference suppression. Compared with traditional methods, our method overcomes the need for interference prior, can effectively resist various complex interference types and has strong generalization ability. The anti-interference processing results based on simulation and measured data verify the effectiveness of the proposed method for various active main lobe suppression interference and show the superiority of the algorithm proposed here by comparing it with three existing anti-interference methods. Finally, comparing the complexity difference between the proposed and mainstream lightweight neural networks shows that the neural networks designed here have low computational complexity and real-time application prospects.
2023, 12(1): 173-185.
Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
2023, 12(1): 186-196.
The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms.
The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms.
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.
2023, 12(1): 214-234.
The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.
The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.
2023, 12(1): 235-246.
Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar.
Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar.
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Announcements
More- Call for Papers of the 15th Annual Radar Conference of China
- High-resolution SAR Ship Detection Dataset-2.0 are officially released
- Call for Papers of Special Issue on Intelligent Processing of Microwave Remote Sensing Information
- Call for Papers of Radars Electromagnetic Scattering Imaging and Recognition
- Call for Papers of Special Issue on New technology of radar electromagnetic scattering imaging recognition
- Notice on the Recruitment of Academic Editors of "Journal of Radars"
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New Progress in Intelligent Electromagnetic Sensing System
- Editorial Comments of Special Issue on the Information Metamaterials and Their Radar Applications
- The Radar and Microwave Vision Sub-Forum and the 1st Journal of Radars Lecture was held at Nanjing
- The First International Airborne Array Synthetic Aperture Radar 3D Imaging
- The Spaceborne Synthetic Aperture Radar Images of Gaofen-3
- Researcher Chibiao Ding presided over the Radar and Microwave Vision Sub-Forum and gave a special lecture
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