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Synthetic Aperture Radar three-dimensional (SAR 3D) imaging technology can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which have become an important trend in SAR development. After decades of development of SAR 3D imaging technology, many types of 3D imaging methods have been proposed. In this study, the history of SAR 3D imaging technology is systematically reviewed and the characteristics of existing SAR 3D imaging technology are analyzed. Given that the 3D information contained in SAR echo and images is not fully used by existing techniques, a new concept of SAR microwave vision 3D imaging has been proposed for the first time. This new concept is integrated with microwave scattering mechanism and image visual semantics to realize three-dimensional reconstruction, which form the theory and method of SAR microwave vision 3D imaging and can achieve high-efficiency and low-cost SAR 3D imaging. This study also analyzes the concept, goal and key scientific problems of SAR microwave vision 3D imaging and provides a preliminary solution, which will contribute in several ways to our understanding of SAR 3D imaging and provide the basis for further research. Synthetic Aperture Radar three-dimensional (SAR 3D) imaging technology can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which have become an important trend in SAR development. After decades of development of SAR 3D imaging technology, many types of 3D imaging methods have been proposed. In this study, the history of SAR 3D imaging technology is systematically reviewed and the characteristics of existing SAR 3D imaging technology are analyzed. Given that the 3D information contained in SAR echo and images is not fully used by existing techniques, a new concept of SAR microwave vision 3D imaging has been proposed for the first time. This new concept is integrated with microwave scattering mechanism and image visual semantics to realize three-dimensional reconstruction, which form the theory and method of SAR microwave vision 3D imaging and can achieve high-efficiency and low-cost SAR 3D imaging. This study also analyzes the concept, goal and key scientific problems of SAR microwave vision 3D imaging and provides a preliminary solution, which will contribute in several ways to our understanding of SAR 3D imaging and provide the basis for further research.
Two-Dimensional (2-D) autofocus is an important guarantee for high-resolution imaging of airborne Synthetic Aperture Radar (SAR) under high maneuvering conditions. The existing 2-D autofocus approaches for bistatic SAR blindly estimate the phase error and do not fully utilize the prior knowledge on phase structure. In this paper, a new interpretation of the Polar Format Algorithm (PFA) for general bistatic SAR imaging is presented. From the viewpoint of Residual Cell Migration (RCM), PFA is converted into 2-D decoupling. By utilizing this new formulation, we analyze the effect of range and azimuth resampling on the residual 2-D phase error and reveal the inherent structure characteristics of the residual 2-D phase error in the wavenumber domain. The 2-D phase error estimation can reduce to one dimensional azimuth phase error estimation. Based on this prior knowledge, a structure-aided 2-D autofocus approach is proposed. Meanwhile, the information of all the data is fully excavated by averaging sub-band data when the azimuth phase error is being estimated. Compared with the existing algorithms, both the parameter estimation precision and computational efficiency are significantly improved. Experimental results clearly demonstrate the correctness of the theoretical analysis and the effectiveness of the proposed method. Two-Dimensional (2-D) autofocus is an important guarantee for high-resolution imaging of airborne Synthetic Aperture Radar (SAR) under high maneuvering conditions. The existing 2-D autofocus approaches for bistatic SAR blindly estimate the phase error and do not fully utilize the prior knowledge on phase structure. In this paper, a new interpretation of the Polar Format Algorithm (PFA) for general bistatic SAR imaging is presented. From the viewpoint of Residual Cell Migration (RCM), PFA is converted into 2-D decoupling. By utilizing this new formulation, we analyze the effect of range and azimuth resampling on the residual 2-D phase error and reveal the inherent structure characteristics of the residual 2-D phase error in the wavenumber domain. The 2-D phase error estimation can reduce to one dimensional azimuth phase error estimation. Based on this prior knowledge, a structure-aided 2-D autofocus approach is proposed. Meanwhile, the information of all the data is fully excavated by averaging sub-band data when the azimuth phase error is being estimated. Compared with the existing algorithms, both the parameter estimation precision and computational efficiency are significantly improved. Experimental results clearly demonstrate the correctness of the theoretical analysis and the effectiveness of the proposed method.
Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions. Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions.
Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors. Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.
In this paper, a fast parameter estimation algorithm for near-field non-circular signals is proposed, which is based on symmetric, uniform, and linear array, that decouple the near-field steering vector via non-circularity and symmetry. Then, the position parameters are estimated based upon polynomial rooting technique instead of traditional spectral search. After fixing the array structure, the parameters for estimation equation are ascertained. Then the values of position parameters are obtained by solving the polynomial equations; consequently, computational difficulty is alleviated effectively. In addition, the Degree-Of-Freedom (DOF) is increased by utilizing the non-circularity characteristics of the impinging sources. It can be seen from the performance analysis and computer simulation experiments that the proposed algorithm can resolve more near-field non-circular signals and can improve parameter estimation performance, which is closer to the Cramer–Rao Bound (CRB) estimation. In this paper, a fast parameter estimation algorithm for near-field non-circular signals is proposed, which is based on symmetric, uniform, and linear array, that decouple the near-field steering vector via non-circularity and symmetry. Then, the position parameters are estimated based upon polynomial rooting technique instead of traditional spectral search. After fixing the array structure, the parameters for estimation equation are ascertained. Then the values of position parameters are obtained by solving the polynomial equations; consequently, computational difficulty is alleviated effectively. In addition, the Degree-Of-Freedom (DOF) is increased by utilizing the non-circularity characteristics of the impinging sources. It can be seen from the performance analysis and computer simulation experiments that the proposed algorithm can resolve more near-field non-circular signals and can improve parameter estimation performance, which is closer to the Cramer–Rao Bound (CRB) estimation.
To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original two-stage weighted least squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound(CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error. To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original two-stage weighted least squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound(CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error.
At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed. At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios. Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies. Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies.
Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm. Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm.
Aircraft wake are a couple of counter-rotating vortices generated by a flying aircraft, which can be very hazardous to a follower aircraft. The detection of it is regarded as a key issue for airport capacity improvement and air traffic safety management. To this end, we constructed a Lidar detection based aircraft wake vortex parameter-retrieval system, which can be used to retrieve the vortex-core positions and circulations from detected data. Furthermore, dynamics, scattering and Lidar echo simulation modules were built to validate the parameter-retrieval algorithms. Results show that the proposed system performs well and runs steadily, which can serve as a good tool for aircraft wake vortex characterization, prediction, and is very helpful to establish dynamic wake separation in air traffic management. Aircraft wake are a couple of counter-rotating vortices generated by a flying aircraft, which can be very hazardous to a follower aircraft. The detection of it is regarded as a key issue for airport capacity improvement and air traffic safety management. To this end, we constructed a Lidar detection based aircraft wake vortex parameter-retrieval system, which can be used to retrieve the vortex-core positions and circulations from detected data. Furthermore, dynamics, scattering and Lidar echo simulation modules were built to validate the parameter-retrieval algorithms. Results show that the proposed system performs well and runs steadily, which can serve as a good tool for aircraft wake vortex characterization, prediction, and is very helpful to establish dynamic wake separation in air traffic management.
Owing to their strong anti-stealth performance, good concealment and strong survivability, passive radar systems have a wide range of applications in both military and civilian fields. We propose a method of target detection for passive radar systems which is based on the characteristics of these systems and the track-before-detect concept. This method accumulates information to effectively detect weak targets with low signal-to-noise ratios and meet real-time requirements. First, we discretize the state space, then perform recursive Bayesian filtering to transfer and accumulate target-state information between multiple frames. Lastly, the information entropy is used to determine whether the target exists, thereby avoiding reliance on a prior assumption about the transition probability model between the existence and the absence of the target. This method is simple to implement and has low computational complexity and high parallelism. The experimental results indicate that the proposed method has a short running time and strong real-time performance, as well as good detection performance and robustness. Owing to their strong anti-stealth performance, good concealment and strong survivability, passive radar systems have a wide range of applications in both military and civilian fields. We propose a method of target detection for passive radar systems which is based on the characteristics of these systems and the track-before-detect concept. This method accumulates information to effectively detect weak targets with low signal-to-noise ratios and meet real-time requirements. First, we discretize the state space, then perform recursive Bayesian filtering to transfer and accumulate target-state information between multiple frames. Lastly, the information entropy is used to determine whether the target exists, thereby avoiding reliance on a prior assumption about the transition probability model between the existence and the absence of the target. This method is simple to implement and has low computational complexity and high parallelism. The experimental results indicate that the proposed method has a short running time and strong real-time performance, as well as good detection performance and robustness.
Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD. Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification. Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results. Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results.
With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed. With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed.
In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance. In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.
The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter. The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter.
To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed. To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed.
The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment. The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment.
This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar. This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar.