Moving Target Detection Performance Research Articles

Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.

Spaceborne distributed aperture radar maneuvering target detection approach with space-time 2D hybrid integration technique

Moving target detection in spaceborne distributed aperture radar (SBDAR) is a challenging task in civilian and military fields. The common issues are that the current methods suffer from the large range cell migration (RCM) and time-varying Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain, which is caused by distributed aperture radar detection configuration and the maneuvering characteristics of moving targets. To address these issues, a novel SBDAR moving target detection approach via space–time hybrid integration (STHI) is proposed in this paper. Compared with single or bistatic radar mode, the utilization of multiple satellites provides an improvement in moving target detection performance, together with the capabilities of localization and parameters estimation. The proposed method mainly includes the following three steps. First, the geometric and signal models of an observed maneuvering target are accurately established in a SBDAR framework, where the space-time 2D RCM and DFM are analyzed. Based on that, the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT) are developed to correct the RCM and DFM, achieving an efficient time dimension coherent integration for maneuvering target. After that, by dividing the radar detection area, the spatial projection method is adopted to integrate the moving target energy in space dimension. Finally, the simulation results verify the effectiveness and advantages of the proposed method.

Moving target detection method based on CUR‐RPCA for missing array elements

Moving target detection performance is seriously affected by missing array elements for multichannel synthetic aperture radar (Multi-SAR) systems. Meanwhile, there is a contradiction between the accuracy of data recovery and the computational burden in traditional methods. To solve the problems, a moving target detection method based on CUR-RPCA for missing array elements is proposed in this paper. First, key nodes of the echo data are extracted and rearranged into a low-rank matrix. Then we uses the low-rank characteristic to complete key nodes of data. Subsequently, complete echo data is constructed by utilising recovered key nodes of data and CUR decomposition. Therefore using the ideas of CUR and RPCA, a novel optimization problem is constructed to achieve separation of moving targets and clutter while recovering data. In addition, only key nodes of the data is completed to achieve the performance of all data recovery, so that the disadvantage of low computational efficiency has been overcome for traditional matrix completion methods. Finally, the superior clutter suppression performance of the proposed method is verified by several numerical simulations and comparative studies.

Joint Design Method of Transmit-Receive for Airborne MIMO Radar Based on Feasible Point Pursuit

Consider the moving target detection performance degradation of airborne multiple-input multiple-output (MIMO) radar in the presence of inaccurate target prior information. This paper proposes a joint design method of transmit waveform and receive filter bank of airborne MIMO radar based on feasible point pursuit successive convex approximation (FPP-SCA). Firstly, a set of receive filter banks is designed in the region where the target may appear on the angle-Doppler plane, and the worst-case output signal-to-clutter-plus-noise ratio (SCNR) is maximized as the optimization criterion. Secondly, considering the energy constraint and similarity on the transmit waveform, the maximin joint design problem is formulated to improve the robustness of the MIMO space-time adaptive processing (STAP) radar against the uncertainty of target parameters. Finally, an FPP-SCA algorithm is employed to solve the maximin nonconvex joint design problem. Simulation results demonstrate the effectiveness of the proposed method in terms of better output SCNR, lower computational load, and more robustness against the errors of target parameters.

Multichannel Signal Modeling and AMTI Performance Analysis for Distributed Space-Based Radar Systems

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

Adaptive Channel Balancing Algorithm Based on 2-D Gaussian Kernel Function

The inherent magnitude and phase errors between channels in the multichannel synthetic aperture radar/ground moving target indication (SAR/GMTI) system will affect the clutter suppression and moving target detection performance of the system. To solve this problem, this letter proposes an adaptive channel balancing algorithm based on the 2-D Gaussian kernel function. First, the iterative reweighted least squares (IRLS) algorithm is used to estimate and compensate the inherent linear phase error that varies with the Doppler frequency in the range-Doppler domain. Then the Gaussian statistical analysis of the residual phase errors is performed in the image domain after the azimuth processing. According to the coupling characteristics of the range and azimuth of the SAR image, the correlation coefficient and the standard deviations of the phase errors in the range and azimuth directions are computed, and a 2-D Gaussian kernel function is constructed to estimate and compensate the residual phase errors. Finally, a mean filter is used to compensate the 2-D magnitude errors. This algorithm can eliminate the magnitude and phase errors and improve the coherence between different channels. The real airborne SAR data demonstrate the effectiveness of the proposed algorithm.

Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

The detection of small moving objects in long-range infrared videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we summarize the investigation of efficient ways to enhance the performance of small target detection in long-range and low-quality infrared videos containing moving objects. In particular, we focus on unsupervised, modular, flexible, and efficient methods for target detection performance enhancement using motion information extracted from optical flow methods. Three well-known optical flow methods were studied. It was found that optical flow methods need to be combined with contrast enhancement, connected component analysis, and target association in order to be effective for target detection. Extensive experiments using long-range mid-wave infrared (MWIR) videos from the Defense Systems Information Analysis Center (DSIAC) dataset clearly demonstrated the efficacy of our proposed approach.

Riemannian Geometric Optimization Methods for Joint Design of Transmit Sequence and Receive Filter on MIMO Radar

In this paper, we study the joint design of a transmit sequence and a receive filter for an airborne multiple-input multiple-output (MIMO) radar system to improve its moving target detection performance in the presence of signal-dependent interference. The optimization problem is formulated to maximize the output signal-to-noise-plus-interference ratio (SINR), subject to the waveform constant-envelope (CE) constraint. To address the challenge of this non-convex problem, we propose a novel optimization framework for solving the problem over a Riemannian manifold which is the product of complex circles and a Euclidean space. Manifold optimization views the constrained optimization problem as an unconstrained one over a restricted search space. The Riemannian gradient descent algorithms and the Riemannian trust-region algorithm are then developed to solve the reformulated problem efficiently with low iteration complexity. In addition, the proposed manifold-based algorithms provably converge to an approximate local optimum from an arbitrary initialization point. Numerical experiments demonstrate the algorithmic advantages and performance gains of the proposed algorithms.

Knowledge Aided Covariance Matrix Estimation via Gaussian Kernel Function for Airborne SR-STAP

In practical airborne radar, the interference signals in training snapshots usually lead to inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), which seriously degrade radar performance and even occur target self-nulling phenomenon. To solve this problem, a knowledge-aided sparse recovery (SR) STAP algorithm based on Gaussian kernel function is developed. The proposed method distinguishes clutter components and interference signals in training snapshots by the priori knowledge that the clutter components are distributed along the clutter ridge, which dislodges interference signals from training snapshots by Gaussian kernel similar degree. Thus, the CCM is estimated by utilizing these snapshots. Finally, the proposed STAP weight vector is built, which is convenient for the subsequent signal processing. The experimental results were performed to verify the effectiveness and superiority of the proposed method. The test results also show that the proposed algorithm completely removes the interference signals, accurately estimates CCM, and improves the moving target detection performance in small-sample and non-homogeneous clutter environments.

Polarisation‐space‐time adaptive processing for heterogeneous clutter suppression of airborne‐phased array radar

Conventional space-time adaptive processing (STAP) achieves clutter suppression by combining spatial signals and the temporal pulses. However, extreme slow-moving target cannot be effectively detected by space-time joint filtering in ambiguous and non-broadside clutter environment for airborne-phased array radar. By increasing the polarisation information in clutter suppression, improved moving target detection performance can be obtained by the polarisation-space-time adaptive processing (P-STAP). In this study, the performance of P-STAP for airborne-phased array radar is analysed. The polarised signal model of airborne-phased array radar is firstly established and then clutter suppression principle of P-STAP is derived in detailed. Afterwards, the performance of P-STAP is investigated and compared with conventional STAP in ambiguous clutter environment. It is verified that the P-STAP can outperform the conventional STAP for slow moving target detection by three-domain joint processing, especially for ambiguous and non-broadside clutter environment for airborne-phased array radar.

A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar

In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

A Fast Iterative Three-Dimensional Joint Domain Localized Method in Airborne MIMO Radar

In general, space–time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom. However, the performance improvement is accompanied by the dramatic increase in computational cost and training sample demanding. Though reduced-dimensional STAP can alleviate these problems, its performance will still be heavily degraded for insufficient training sample support, which will frequently be met in the airborne MIMO radar. Hence, in this work, by utilizing the special structure of the MIMO radar signal, the STAP filter coefficients of the conventional joint domain localized processing method are constrained as the Kronecker product form so that the computational cost and training sample demanding are further reduced. Then, the tri-iterative algorithm is applied to find the desired solution. Simulation results demonstrate the effectiveness of the proposed method.

A Robust STAP Method Based on Alternating Projection and Clutter Cancellation

Space-time steering vector mismatch of targets usually happens in the space-time adaptive processing (STAP) technique, causing space-time beam distortion. Due to the effects of clutter spectrum broadening and jamming, performance of the conventional STAP method deteriorates dramatically. This paper proposes a novel robust STAP method to improve the performance of clutter suppression and moving target detection. The proposed method employs the alternating projection and clutter cancellation method, to reconstruct the clutter plus jamming plus noise covariance matrix (CJNCM) and re-estimate the target steering vector. Firstly, reconstruction of CJNCM consists of summing the eigenvectors of the projection operator, combining the sample covariance matrix and the integral covariance matrix (ICM) of specific region. Secondly, the re-estimated target steering vector is obtained by estimating the dominant eigenvector of the ICM of the Region of Interest (ROI), which contains the target signal purely because CJNCM is cancelled before estimation. Finally, the robust space-time adaptive filtering weight vector is calculated through the MVDR method with the reconstructed CJNCM and the re-estimated target steering vector. Simulation results indicate that the proposed algorithm shows robust performance against space-time steering vector mismatch, better output signal-to-noise ratio performance and better moving target detection performance than the traditional robust STAP method.

Preliminary Research of Low-RCS Moving Target Detection Based on Ka-Band Video SAR

Conventional synthetic aperture radar (SAR) ground moving target detection is only effective for targets with high radar cross section (RCS), and its performance is degraded by focusing distortion caused by the motion of the target. This letter proposes a new low-RCS moving target detection method which exploits the target’s shadow using high-resolution sequential images captured by a Ka-band video SAR system. First, the characteristics of target shadow are investigated, which are mainly influenced by the size of the target, the incidence of the radar beam, and the target velocity. Then the presented method based on video SAR system is described, and its moving target detection performance is also analyzed. As a preliminary study, a simulated experiment is conducted in which the signal of a low-RCS moving target is simulated and inserted into real airborne Ka-band video SAR images. The results demonstrate the feasibility of the proposed method for low-RCS moving target detection.

Robust non‐homogeneity detector based on reweighted adaptive power residue

Space time adaptive processing (STAP) is an excellent technique for improving ground moving target detection performance of airborne radar. However, covariance matrix estimation required by STAP is commonly corrupted by the presence of target-like signals (i.e. outliers), and thus resulting severe performance degradation. To overcome this problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed, where an adaptively reweighted scheme is employed to training data set. Therefore, the deleterious effect of outliers on the covariance estimation is eliminated and the robustness of the non-homogeneity detector is guaranteed. Performance analysis using the simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the radar detection performance in a dense target environment.

Efficient imaging algorithm for airborne CSSAR/GMTI systems

Airborne circular stripmap synthetic aperture radar (CSSAR) has the advantages of short revisit time and large coverage, and thus is an attractive tool for wide-area surveillance and ground moving-target indication (GMTI). To enhance the CSSAR/GMTI system's surveillance capability and moving-target detection performance, an efficient imaging algorithm is proposed. The proposed algorithm can finely focus the stationary scene and coarsely focus multiple moving targets simultaneously without a priori knowledge of the targets’ motion parameters and position parameters. Moreover, the proposed algorithm is interpolation free and can be efficiently implemented using only complex multiplications and fast Fourier transforms. Simulation results validate the proposed algorithm.

Space–time interference analysis and suppression for airborne passive radar using transmissions of opportunity

This study presents the space–time snapshot models for the interfering passive signals received by the airborne passive radar which uses a ground-based stationary non-cooperative transmitter. The random range sidelobes couplings of the direct path and of the strong clutter signals into further range cells are major concerns in moving target detection performance. The least squares-based adaptive interference cancellation technique is proposed to efficiently suppress the direct path, strong clutter and Doppler-shifted strong clutter signals present at each antenna element prior to matched filter processing. In mitigating the interfering signals, their corresponding random range sidelobes will also be suppressed by the same amount. Ground-based moving passive radar trials were conducted to validate and ascertain the effectiveness of the proposed adaptive interference cancellation algorithm.

STUDY STAP ALGORITHM ON INTERFERENCE TARGET DETECT UNDER NONHOMOGENOUS ENVIRONMENT

In conventional statistical STAP algorithms, the existence of interference target in training samples will lead to signal cancellation, resulting in the output SCR falling and the moving target detection performance degrading. The nonhomogeneity detector is an efiective way to restrain the outlier, which can improve the covariance matrix estimation by detecting the samples containing outliers and rejecting them, and improve the STAP performance. A new interference target detection algorithm is proposed in this paper, the outlier detection is realized by using the samples' data phase information. Compared with traditional method, the improved algorithm is more sensitive to interfering target with difierent azimuth and intensity. Simulation results demonstrate the validity of this improved method.

Parametric Velocity Synthetic Aperture Radar: Multilook Processing and Its Applications

Based on a parametric model and some optimum methods, it has been previously proved that parametric velocity synthetic aperture radar (VSAR) may improve the performances of moving target detection and parameter estimation simultaneously. In this paper, multilook processing is studied for parametric VSAR. At first, statistical signal models are established for azimuth multilook processing (AMLP) and range multilook processing (RMLP), respectively. By combining the multiple AMLP sublook pixel vectors, it is shown that the clutter parameter estimation accuracy can be further improved via the maximum-likelihood estimation methods. Meanwhile, based on the adaptive implementation for the optimum processing of VSAR, RMLP can be used to improve slowly moving target detection performance via the noncoherent integration of multiple range sublooks. Furthermore, it is shown that the Doppler frequencies of moving targets vary linearly with different range sublooks due to the different carrier frequencies of sublooks. Therefore, based on a proposed novel two-step multilook diversity (TS-MLD) estimator for RMLP, the "azimuth location ambiguity" of VSAR can be well resolved via least squares linear regression, without configuration change of the conventional VSAR. Also, the estimation accuracy of target's unambiguous Doppler frequency, as well as target's azimuth location, is derived for the proposed TS-MLD method. Finally, numerical experiments and scene simulations are provided to demonstrate the effectiveness of the proposed multilook-based methods.

Post-adaptive processing time-delay beamforming for clutter suppression in airborne radar

A novel beamforming architecture is presented in which time delay steering is applied after the application of space-time adaptive processing (STAP) in a phased array for airborne radar. Improved clutter suppression and slow moving target detection performance can be achieved over that associated with STAP and conventional time delay steering in wideband sideways looking applications. The architecture also provides a computationally efficient means of applying time-delay beamforming in conjunction with STAP in applications where multiple receive beams are required.