Reduced-dimension Space-time Adaptive Processing Research Articles

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used.

Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm.

An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System

In this paper, a joint maritime moving target detection and imaging approach, referred to as the fast inverse synthetic aperture radar (ISAR) imaging approach, based on the multi-resolution space−time adaptive processing (STAP), is proposed to improve the target detection performance and the target imaging efficiency in an airborne radar system. In the target detection stage, the sub-band STAP is introduced to improve the robustness of clutter suppression and to enhance the target output power with the decreased range resolution, by which the coarse estimation of target range-Doppler (R-D) location is obtained as the prior knowledge. In the following target imaging stage, the ISAR imaging is applied in the localized R-D zone surrounding with the target location. However, it is difficult to directly apply ISAR imaging with the conventional R-D algorithm because the slow-moving maritime target cannot be separated from the clutter interference in the Doppler frequency dimension. In this regard, the full-band STAP is applied in the R-D two-dimensional frequency domain for the simultaneous clutter suppression and high-resolution ISAR imaging, in which the envelope alignment and phase compensation are achieved by adaptive match filtering with the target Doppler frequency coarse estimation. Moreover, the reduced-dimension STAP applied in the target-surrounded localized Doppler frequency zone gives facilities for alleviating the computation burden. Simulation results corroborate the effectiveness of the proposed method.

Angle-Doppler Channel Selection Method for Reduced-Dimension STAP Based on Sequential Convex Programming

This letter considers the dimension reduction for the space-time adaptive processing (STAP). An angle-Doppler channel selection strategy to maximize the lower bound of the output signal-to-clutter-plus-noise ratio (SCNR) is proposed. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form based on the majorization-minimization (MM) algorithm and solve it in an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance, especially when the number of samples are limited.

Joint design of transmit beamspace and reduced‐dimension receiver filter for MIMO radar STAP

In this article, the problem of the transmit beamspace (TB) matrix design in multiple-input multiple-output (MIMO) radar is considered. To reduce the computational burden and to focus the energy within optimal spatial sector, a joint design method of the TB matrix and a reduced-dimension space-time adaptive processing (STAP) filter is proposed based on the maximum signal-to-clutter-plus-noise ratio (SCNR). Firstly, the signal model of the reduced-dimension STAP for TB-based MIMO radar is established. Then, using the maximum SCNR as the design metric, the joint design model of the TB matrix and the reduced-dimension STAP filter is presented. Furthermore, the cyclic optimisation method is exploited to deal with the non-convex joint design problem with the energy constraint. Finally, simulation experiments demonstrate the joint design method of the TB matrix and the STAP filter not only has better output SCNR performance but also owns lower computational burden when compared with the other existing methods.

Adaptive Weight Vector Solving Based on the Summation of Partial Eigenbeams in STAP

For the side-looking airborne or spaceborne early warning radar, Space-time adaptive processing (STAP) is a well-established technique. The adaptive weight vector can be written in the form of summation that equivalents to the inversion of covariance matrix. In order to reduce the computational complexity of the adaptive weight vector, this paper proposes a modified calculation method based on the weighted sum of partial eigenbeams according to the array model of the radar system. Both the loss of signal-to-interference and noise ratio (LSINR) and adapted patterns are used as performance metrics. Computer simulations verified the effectiveness of the method and its adaptability in traditional full-dimension, fixed-structure reduced dimension STAP.

A Recursive Angle-Doppler Channel Selection Method for Reduced-Dimension Space-Time Adaptive Processing

This article considers the problem of target detection for applications with limited training samples. Dimensionality reduction in the angle-Doppler domain is considered. An algorithm for selecting the best angle-Doppler channels that maximize the output signal-to-clutter-plus-noise ratio in space-time adaptive processing (STAP) is proposed. Compared with several existing STAP methods, the proposed method can achieve better clutter suppression performance at lower computational complexities when the degrees of freedom of the STAP system are fixed. Moreover, the proposed method can help address the issue of training data shortage, which may be particularly attractive for heterogeneous scenarios. Simulations are conducted for validating the proposed method and demonstrating their high performance.

Data‐dependent reduced‐dimension STAP

Space-time adaptive processing (STAP) is supposed to be a key technique for detection of slow-moving targets in airborne radar applications. To overcome the difficulties of obtaining lots of independent and identical distributed secondary data samples, many sub-optimal STAP algorithms are proposed such as reduced-dimension STAP, reduced-rank STAP, or the sparse-recovery-based STAP. In this study, based on the observation that the STAP filter weight is sparse, a novel clutter-dependent reduced-dimension STAP is proposed. In the proposed approach, the sparse feature of the weight vector in the angular-Doppler domain is utilised to design the reduced-dimension transform matrix. Simulation results show that the output signal-to-interference-plus-noise ratio loss is just 3 dB with only one secondary data sample in the extremely heterogeneous clutter environments.

Exploiting persymmetry for JDL‐STAP

Joint domain localisation (JDL) is a popular reduced-dimension space-time adaptive processing (STAP) technique for clutter suppression in an air-borne radar system. Here, the authors develop an improved JDL method by exploiting persymmetric covariance matrix, referred to as persymmetric joint domain localisation (Per-JDL), in order to make maximum use of training samples and further improve the STAP performance under small training data support. The proposed algorithm is verified to be efficient in training-limited scenarios by simulation results.

Reduced Dimension STAP Based on Sparse Recovery in Heterogeneous Clutter Environments

For airborne-phased array radar systems, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in the strong clutter background. However, practical application environments are always heterogeneous and have offered a severe challenge to the implementation of STAP. To address this reality, a data-dependent reduced dimension STAP approach based on sparse recovery (SR) is proposed in this paper. To take a full account of the heterogeneous environments, we consider the extremely heterogeneous case in which only one single snapshot is available. There is no doubt that, compared with the clutter covariance matrix (CCM) calculated directly by a single snapshot, the SR technique can provide a more accurate estimate. However, we should come to realize that this estimation is not accurate enough to adaptive processing according to the presented simulation results in lots of literature, and it has been demonstrated that the performance of traditional SR-based STAP degrades dramatically when only a snapshot is available. In the proposed approach, the CCM estimated by SR technique is utilized to design the reduced dimension transformation matrix rather than to calculate adaptive weights as the traditional SR-based STAP. The relatively accurate CCM can provide better support for the design of reduced dimension transformation matrix. From the simulation results, the proposed approach can achieve great performance of clutter suppression and target detection with only a single snapshot compared with several typical STAP algorithms. It is worthwhile pointing out that the proposed approach can also be applied when multiple snapshots are available and the performance improves with increasing number of available snapshots.

Reduced‐dimension space‐time adaptive processing for airborne radar with co‐prime array

Space-time adaptive processing (STAP) for airborne radar with co-prime arrays is shown to have excellent superiority compared to traditional STAP with uniform linear array radar. However, high arithmetic computational complexity and large amount of training data are required in this approach. This motivates the authors to present a new approach which is relatively low computational load and fast convergence with satisfactory performance. Specifically, a reduced-dimension transformation in Doppler domain is incorporated into the existing approach. The reduced-dimension interference covariance matrix and target steering vector are then achieved by performing the reduced-dimension process, and hence two reduced-dimension STAP filters are designed using the derived interference covariance matrix and target-steering vector. Numerical simulations are carried out to reveal the superiority of the proposed approach.

A Geometric Barycenter-Based Clutter Suppression Method for Ship Detection in HF Mixed-Mode Surface Wave Radar

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

Reduced‐dimension STAP using a modified generalised sidelobe canceller for collocated MIMO radars

This study addresses the problem of beam-space post-Doppler reduced-dimension (RD) space-time adaptive processing (STAP) using a modified generalised sidelobe canceller (GSC) in an airborne collocated multiple-input multiple-output (MIMO) radar. Steering vectors corresponding to the nulls of beamforming are used to construct the blocking matrix (BM) of the GSC processor with optimal signal to clutter plus noise ratio. However, it is difficult to determine the beampattern nulls for a collocated MIMO radar which is equivalent to a virtual array with coherent amplitude weights. To build a valid BM of the GSC-based STAP processor, the authors propose a BM modification method based on the modified Gram-Schmidt orthogonalisation algorithm. The authors prove that the modified RD-GSC processor can achieve the performance equal to that of the fully optimal processor conditionally. The authors also show that the proposed method has reduced computational complexity and fast convergence rate. Numerical simulations are presented to demonstrate the effectiveness of the proposed methods.

Reduced-dimension space-time adaptive processing with sparse constraints on beam-Doppler selection

Abstract State-of-the-art space-time adaptive processing (STAP) algorithms devised in the beam-Doppler domain are confined by fixing the beam-Doppler cells used for adaptation, which may suffer from performance degradation. To overcome this drawback, a novel STAP algorithm in the beam-Doppler domain is proposed. The proposed algorithm adopts a generalized sidelobe canceller structure, and the filter design is formulated as a sparse representation problem by imposing a sparse constraint on the weight vector. As the sparse constraint enforces most of the elements in the weight vector to be zero (or sufficiently small in amplitude), the proposed algorithm can adaptively select the best beam-Doppler cells for adaptation, and thus it falls under the category of reduced-dimension approach. Simulation results illustrate that the proposed algorithm outperforms the existing STAP approaches with fixed beam-Doppler localized processing.

Local Degree of Freedom of Clutter for Reduced-Dimension Space-Time Adaptive Processing with MIMO Radar

Degree of freedom (DOF) of clutter in the reduced-dimension (RD) domain, which is called local DOF (LDOF), is of great importance for RD MIMO-STAP (space-time adaptive processing for multiple-input multiple-output radar) algorithms. In this paper, the LDOF equivalence of different RD MIMO-STAP algorithms are firstly proved, and then a generalized LDOF estimation rule under different conditions is developed to estimate the clutter LDOF for MIMO radar effectively. The accuracy of the proposed rule is verified, and how to design RD MIMO-STAP processors under the guidance of the proposed rule is presented through numerical simulations.

Multichannel GMTI techniques to enhance integration of temporal signal energy for improved target detection

Improvements in target detection are typically achieved by increasing the signal-to-interference-plus-noise ratio (SINR) of the received signal. For a small radar platform with power and aperture constraints, increases in SINR must come through temporal integration. Two techniques are presented here for enhancing the integration of temporal signal energy to improve detection of weak, slow-moving targets over conventional methods. Both techniques involve modifications to pre-Doppler space-time adaptive processing (STAP), a reduced-dimension STAP method that is implementable in real-time on a power-constrained platform. The two modifications include (i) development of an alternative pre-Doppler temporal weighting method, based on linear prediction, to increase the SINR of the pre-Doppler temporal output signal and (ii) development of an extended dwell temporal processing (EDTP) algorithm to integrate temporal signal energy collected over a long dwell. The EDTP algorithm is based on frequency domain analysis of the extended dwell time signal; the detection decision exploits the differences in the distribution of signal energy and noise energy over an extended dwell. EDTP algorithm performance is superior to traditional temporal processing methods in many practical scenarios: the EDTP algorithm detection rate is three times the detection rate of non-coherent integration for very low-velocity, low-SNR targets.

Sparse Representation Based Algorithm for Airborne Radar in Beam-Space Post-Doppler Reduced-Dimension Space-Time Adaptive Processing

An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm based on sparse representation for ground clutter suppression in airborne radar is proposed in this paper. First of all, the principle and problems of sample matrix inversion-based STAP and sparse representation (SR)-based STAP algorithms are reviewed. Then, the conception of the local space-time spectrum (LSTS) of clutter is considered by exploiting the intrinsic sparsity nature of clutter in local beams and the Doppler domain. To estimate the LSTS using the sparse representation technique in a cost-effective way, a variable space-time mask matrix is designed. Finally, the reduced-dimension clutter plus noise covariance clutter matrix and the corresponding adaptive weight vector are calculated based on the estimated LSTS. Numerical results with both simulated data and Mountain-Top data demonstrate that the new algorithm provides an excellent performance of clutter suppression and moving target detection with only one training range cell and significant computational savings compared with existing SR-based STAP algorithms.

Beam‐space reduced‐dimension space‐time adaptive processing for airborne radar in sample starved heterogeneous environments

A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (STAP) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed approach is data dependent. Based on maximum cross-correlation energy metric, the significance of each spatial-Doppler channel is evaluated, and the auxiliary channels are selected step-by-step through utilising iteration. For the sake of achieving better performance as much as possible, the proposed approach will select two auxiliary channels at the first step, and select one channel at the next each step. Due to that the explicit physical meaning is very important for a STAP algorithm, the physical meaning of the maximum cross-correlation energy metric is discussed, and the fact that the local optimal output signal-to-interference-noise (SINR) performance can be assured by the maximum cross-correlation energy metric is proved theoretically. The simulations demonstrated that the output SINR loss of the proposed approach is about −1.9 dB when only two auxiliary channels are selected. Consequently, the proposed approach can reduce the requirement of the sample support dramatically. This will be more obvious advantage for the practical application in heterogeneous clutter environments where the number of secondary samples is extremely limited.

Robust two-stage reduced-dimension STAP algorithm and its performance analysis

A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

Complexity reduction and clutter rank estimation for MIMO-phased STAP radar with subarrays at transmission

Focused on reducing the complexity and the number of required training samples of space-time adaptive processing (STAP) technique, four typical classes of reduced-dimension (RD) algorithms are extended to the multiple-input multiple-output (MIMO) phased radar with transmitting subarrays. Then, a simple rule to estimate clutter rank in the reduced-dimension domain is derived. Some former rules in literature can be viewed as special cases of the proposed rule. Influences of orthogonality degradation of transmitting waveforms and other non-ideal factors are also analyzed. With this rule, more appropriate parameters of RD STAP processors can be determined, and thus better trade-off between system complexity and clutter suppression performance can be achieved. At last, simulated results with different conditions are utilized to assess the clutter suppression performance of the proposed algorithms and demonstrate the accuracy and applications of the proposed estimation rule.