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Editor Work2017 Vol. 6, No. 3
Previous Issue
2017, 6(3): 229-238.
Abstract
850KB
Recently emerging, high maneuvering near space targets have many characteristics that differ from conventional targets, like ultra-high speed, high-maneuverability, ultra-far range, low Radar Cross Section (RCS), plasma sheath, ionosphere layer pollution, and cosmic ray interference. Based on general signal modeling for near space targets of ground-based, airborne, and spaceborne radars, this paper proposes novel focus-before-detection methods with respect to a distributed radar network, multi-dimensions, multiple targets, micro motion, varied model, and non-parametric processing. The proposed FBD based methods can effectively suppress the strong ionosphere layer pollution and active jamming, as well as problems like the scaled effect of echoes, arbitrary motion, aperture fill time, sparse sub-band frequency synthesis, across range cell, across Doppler cell, and across beam width. The proposed Focus-Before-Detection (FBD) based methods can remarkably improve the signal processing performance on target detection, parameter estimation, maneuver tracking, high-resolution imaging, feature extraction, and target recognition. Additionally, they are suitable for both high maneuvering near space targets and conventional targets, and can be applied for both new-generation radars and conventional targets. Therefore, the proposed FBD based methods for high maneuvering near space target detection have both important academic research value and impact a wide variety of applications.
Recently emerging, high maneuvering near space targets have many characteristics that differ from conventional targets, like ultra-high speed, high-maneuverability, ultra-far range, low Radar Cross Section (RCS), plasma sheath, ionosphere layer pollution, and cosmic ray interference. Based on general signal modeling for near space targets of ground-based, airborne, and spaceborne radars, this paper proposes novel focus-before-detection methods with respect to a distributed radar network, multi-dimensions, multiple targets, micro motion, varied model, and non-parametric processing. The proposed FBD based methods can effectively suppress the strong ionosphere layer pollution and active jamming, as well as problems like the scaled effect of echoes, arbitrary motion, aperture fill time, sparse sub-band frequency synthesis, across range cell, across Doppler cell, and across beam width. The proposed Focus-Before-Detection (FBD) based methods can remarkably improve the signal processing performance on target detection, parameter estimation, maneuver tracking, high-resolution imaging, feature extraction, and target recognition. Additionally, they are suitable for both high maneuvering near space targets and conventional targets, and can be applied for both new-generation radars and conventional targets. Therefore, the proposed FBD based methods for high maneuvering near space target detection have both important academic research value and impact a wide variety of applications.
2017, 6(3): 239-251.
To address difficulties in radar signal processing, the effective and efficient detection of low-observable moving targets in complex environments is an ongoing research hotspot. On the one hand, a signal may be extremely weak due to strong clutter and the complex motion of a target, making it hard to separate them in the time and frequency domains. On the other hand, complex coherent integration methods and the heavy computational burden of long-time integration represent challenges for improving radar detection performance with limited resources. High-resolution sparse representation can separate clutter from a moving target with respect to signal sparsity, and can be regarded as an extension of traditional transform-based moving target detection methods. This method has promising application prospects due to the advantages of its high time-frequency resolution, anti-noise property, robustness, and suitability for the analysis of multi-signals. In this paper, we systematically review conventional radar moving target detection methods. Then, we summarize their applications, including sparse representation in clutter property analysis, suppression, moving target detection, signature extraction, and time-frequency analysis. Next, we consider future developments. Finally, we provide some results based on real datasets and existing research.
To address difficulties in radar signal processing, the effective and efficient detection of low-observable moving targets in complex environments is an ongoing research hotspot. On the one hand, a signal may be extremely weak due to strong clutter and the complex motion of a target, making it hard to separate them in the time and frequency domains. On the other hand, complex coherent integration methods and the heavy computational burden of long-time integration represent challenges for improving radar detection performance with limited resources. High-resolution sparse representation can separate clutter from a moving target with respect to signal sparsity, and can be regarded as an extension of traditional transform-based moving target detection methods. This method has promising application prospects due to the advantages of its high time-frequency resolution, anti-noise property, robustness, and suitability for the analysis of multi-signals. In this paper, we systematically review conventional radar moving target detection methods. Then, we summarize their applications, including sparse representation in clutter property analysis, suppression, moving target detection, signature extraction, and time-frequency analysis. Next, we consider future developments. Finally, we provide some results based on real datasets and existing research.
2017, 6(3): 252-258.
The Track-Before-Detect (TBD) algorithm based on the particle filter is proposed for weak extended target detection and tracking in low signal to clutter noise radio. The rod-shaped object is analyzed by dividing the cell on range and azimuth under the Weibull clutter. On the basis of a point target, the likelihood function and particle weights can be obtained by the target spread function. In the TBD algorithm, the binary target variable and the target shape parameters is added to the state vector and the scattering points in the sample collection is given based on the particle filter, which can detect and estimate the target state and the shape parameters under the clutter environment. Simulation results show that the stability of the algorithm is very good.
The Track-Before-Detect (TBD) algorithm based on the particle filter is proposed for weak extended target detection and tracking in low signal to clutter noise radio. The rod-shaped object is analyzed by dividing the cell on range and azimuth under the Weibull clutter. On the basis of a point target, the likelihood function and particle weights can be obtained by the target spread function. In the TBD algorithm, the binary target variable and the target shape parameters is added to the state vector and the scattering points in the sample collection is given based on the particle filter, which can detect and estimate the target state and the shape parameters under the clutter environment. Simulation results show that the stability of the algorithm is very good.
2017, 6(3): 259-266.
Space, time, and phase synchronization problems make weak target detection evenmore difficult in the Non-cooperative Passive Bistatic Radar (NPBR) than in conventional radar systems. Therefore, a time and phase synchronization method based on direct waveform parameter estimation and a weak target detection method based on long time coherent integration of a multi-waveform is presented in this paper. First, a universal pulse extraction method based on differential sequence indexing is proposed. Second, an estimation method for the direct waveform parameters, including pulse width, pulse repetition intervals, bandwidth, carrier frequency, and arrival time, is provided. Therefore, by using the estimated waveform parameters, the NPBR time and phase synchronization can be realized. Moreover, based on the waveform parameter estimation, a weak target detection method based on the generalized Radon-Fourier transform of a multi-waveform is provided. Finally, simulation and real data experiments for verifying the effectiveness of the waveform parameter estimation and weak target detection methods are provided.
Space, time, and phase synchronization problems make weak target detection evenmore difficult in the Non-cooperative Passive Bistatic Radar (NPBR) than in conventional radar systems. Therefore, a time and phase synchronization method based on direct waveform parameter estimation and a weak target detection method based on long time coherent integration of a multi-waveform is presented in this paper. First, a universal pulse extraction method based on differential sequence indexing is proposed. Second, an estimation method for the direct waveform parameters, including pulse width, pulse repetition intervals, bandwidth, carrier frequency, and arrival time, is provided. Therefore, by using the estimated waveform parameters, the NPBR time and phase synchronization can be realized. Moreover, based on the waveform parameter estimation, a weak target detection method based on the generalized Radon-Fourier transform of a multi-waveform is provided. Finally, simulation and real data experiments for verifying the effectiveness of the waveform parameter estimation and weak target detection methods are provided.
2017, 6(3): 267-274.
Differences in target radial velocities of different airborne radar are used for target detection in a multiple Airborne Early Warnings (AEWs) coordinated detection radar system; this subject is currently a popular research topic. In this paper, a collaborative detection radar system model comprised of multiple AEWs with different transmit waveforms is constructed. The Generalized Likelihood Ratio Test (GLRT) based adaptive detector is then proposed and the approximated statistical characters of the detector are analyzed. Finally, the moving target detection performances of three selected transmit waveforms are analyzed by computer simulation; results show that the detection performance of the collaborative detection radar system is influenced by the transmit waveform, and that the detection performances of different velocity targets are different with the same transmit waveform.
Differences in target radial velocities of different airborne radar are used for target detection in a multiple Airborne Early Warnings (AEWs) coordinated detection radar system; this subject is currently a popular research topic. In this paper, a collaborative detection radar system model comprised of multiple AEWs with different transmit waveforms is constructed. The Generalized Likelihood Ratio Test (GLRT) based adaptive detector is then proposed and the approximated statistical characters of the detector are analyzed. Finally, the moving target detection performances of three selected transmit waveforms are analyzed by computer simulation; results show that the detection performance of the collaborative detection radar system is influenced by the transmit waveform, and that the detection performances of different velocity targets are different with the same transmit waveform.
2017, 6(3): 275-284.
Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM), normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF) and ANMF (Adaptive Normalized Matched Filter, ANMF) detectors.
Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM), normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF) and ANMF (Adaptive Normalized Matched Filter, ANMF) detectors.
2017, 6(3): 285-291.
In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix. Generally, Normalized Frobenius Norm (NFN) is used to test the estimated accuracy of different speckle covariance matrix estimators, in which the requirement of a known real covariance matrix is hardly realized in the radar system. Therefore, in this study, a whitening degree evaluation method is proposed wherein the decorrelation of speckle covariance matrix in whitening filter processing of the radar system is fully exploited. It considers the correlation degree among pulses in the whitening clutter vector as the criterion to evaluate the estimate error of the speckle covariance matrix. The proposed method shows consistent conclusions with NFN on simulated data and also avoids limitations of the latter method in real data processing.
In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix. Generally, Normalized Frobenius Norm (NFN) is used to test the estimated accuracy of different speckle covariance matrix estimators, in which the requirement of a known real covariance matrix is hardly realized in the radar system. Therefore, in this study, a whitening degree evaluation method is proposed wherein the decorrelation of speckle covariance matrix in whitening filter processing of the radar system is fully exploited. It considers the correlation degree among pulses in the whitening clutter vector as the criterion to evaluate the estimate error of the speckle covariance matrix. The proposed method shows consistent conclusions with NFN on simulated data and also avoids limitations of the latter method in real data processing.
2017, 6(3): 292-299.
In this paper, we focus on the detection of a moving point-like target embedded in uncertain signal-dependent clutter and develop robust transmit-code and receive-filter designs in slow-time. First, based on the Worst-case Signal-to-Interference-plus-Noise Ratio (W-SINR) when the second-order clutter statistics are uncertain, we establish a high-dimensional transmit-receive optimization model that considers the constant modulus constraint with non-convexity. Next, we propose an Iterative Sequential Optimization (ISO) algorithm. At each iteration, it converts a high-dimensional optimization into multiple one-dimensional fractional programming problems that can be efficiently solved using Dinkelbach’s method. Finally, we use numerical examples to confirm that the ISO can resist the uncertain knowledge of signal-dependent clutter, which enables the radar system to adapt to complicated environments. Moreover, compared to Semi-Definite Relaxation (SDR)-related and randomization methods, the proposed algorithm is superior with respect to both optimized W-SINR and computational time.
In this paper, we focus on the detection of a moving point-like target embedded in uncertain signal-dependent clutter and develop robust transmit-code and receive-filter designs in slow-time. First, based on the Worst-case Signal-to-Interference-plus-Noise Ratio (W-SINR) when the second-order clutter statistics are uncertain, we establish a high-dimensional transmit-receive optimization model that considers the constant modulus constraint with non-convexity. Next, we propose an Iterative Sequential Optimization (ISO) algorithm. At each iteration, it converts a high-dimensional optimization into multiple one-dimensional fractional programming problems that can be efficiently solved using Dinkelbach’s method. Finally, we use numerical examples to confirm that the ISO can resist the uncertain knowledge of signal-dependent clutter, which enables the radar system to adapt to complicated environments. Moreover, compared to Semi-Definite Relaxation (SDR)-related and randomization methods, the proposed algorithm is superior with respect to both optimized W-SINR and computational time.
2017, 6(3): 300-308.
This study considered parameter estimations for micro-motion targets embedded in non-Gaussian noise with a Single Input Multiple Output (SIMO) radar. A novel estimation algorithm based on mutual correntropy was presented and used to derive the micro-perturbation parameters by exploiting the second and higher-order knowledge of the return signals among multiple channels. Compared with a conventional Fourier Transform (FT) method, the method proposed herein had a much higher Signal to Noise Ratio (SNR) gain. In addition, the location was derived by employing the Phase-Comparison Monopulse (PCM) technique. Finally, several numerical results were provided and discussed.
This study considered parameter estimations for micro-motion targets embedded in non-Gaussian noise with a Single Input Multiple Output (SIMO) radar. A novel estimation algorithm based on mutual correntropy was presented and used to derive the micro-perturbation parameters by exploiting the second and higher-order knowledge of the return signals among multiple channels. Compared with a conventional Fourier Transform (FT) method, the method proposed herein had a much higher Signal to Noise Ratio (SNR) gain. In addition, the location was derived by employing the Phase-Comparison Monopulse (PCM) technique. Finally, several numerical results were provided and discussed.
2017, 6(3): 309-315.
In allusion to the coupling relationship among radial velocity variation, range migration, and Doppler spread of a maneuvering target, a Dynamic Programing (DP)-based Weighted Adaptive Coherent Integration (DPWACI) detection method is proposed. The target energy could be efficiently integrated along its trajectory by the proposed method. The main idea of DPWACI is the joint execution of the three operations: the weighted DP procedure that could accurately search the current position and velocity of the target; the improved coherent integration that could calibrate the Doppler shift during the entire process with a large phase spread; and the adaptive step that could make the integration time suitable for each velocity searching bin. The proposed method is applicable to a target with an arbitrary motion without estimating its specific movement parameters. Simulation results and performance comparisons show the exactitude and superiority of the proposed method.
In allusion to the coupling relationship among radial velocity variation, range migration, and Doppler spread of a maneuvering target, a Dynamic Programing (DP)-based Weighted Adaptive Coherent Integration (DPWACI) detection method is proposed. The target energy could be efficiently integrated along its trajectory by the proposed method. The main idea of DPWACI is the joint execution of the three operations: the weighted DP procedure that could accurately search the current position and velocity of the target; the improved coherent integration that could calibrate the Doppler shift during the entire process with a large phase spread; and the adaptive step that could make the integration time suitable for each velocity searching bin. The proposed method is applicable to a target with an arbitrary motion without estimating its specific movement parameters. Simulation results and performance comparisons show the exactitude and superiority of the proposed method.
2017, 6(3): 316-323.
The performance of sparse reconstruction algorithm of compressive sensing in low Signal-to-Noise Ratio (SNR) is lower, and the quality of sparse three-dimensional imaging for forward-looking array synthetic aperture radar in low SNR is reduced greatly. To solve this problem, a validate method of reconstruction algorithm of compressive sensing based on Hough transform is proposed, in which the continuity of the scattering coefficient vector in the two-dimensional space of range direction and slant range direction and the straight line detection method of Hough transform is used, and thus the reconstruction quality of compressive sensing is increased effectively. Also, the simulation experiments indicate that this method can improve the sparse three-dimensional imaging for forward-looking array SAR in low SNR effectively.
The performance of sparse reconstruction algorithm of compressive sensing in low Signal-to-Noise Ratio (SNR) is lower, and the quality of sparse three-dimensional imaging for forward-looking array synthetic aperture radar in low SNR is reduced greatly. To solve this problem, a validate method of reconstruction algorithm of compressive sensing based on Hough transform is proposed, in which the continuity of the scattering coefficient vector in the two-dimensional space of range direction and slant range direction and the straight line detection method of Hough transform is used, and thus the reconstruction quality of compressive sensing is increased effectively. Also, the simulation experiments indicate that this method can improve the sparse three-dimensional imaging for forward-looking array SAR in low SNR effectively.
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