Conventional Space-time Adaptive Processing Algorithms Research Articles

A Novel Dimension-Reduced Space–Time Adaptive Processing Algorithm for Spaceborne Multichannel Surveillance Radar Systems Based on Spatial–Temporal 2-D Sliding Window

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

Low-Complexity Off-Grid STAP Algorithm Based on Local Search Clutter Subspace Estimation

Space–time adaptive processing (STAP) based on sparse recovery (SR-STAP) techniques exhibits significantly better performance than conventional STAP algorithms within a very small number of snapshots. However, when the clutter patches do not locate exactly on the discrete space–time grid points, the performances of SR-STAP algorithms degrade severely. In this letter, a low-complexity off-grid STAP algorithm based on local search clutter subspace estimation is proposed to overcome this issue. In the proposed algorithm, the global atoms are first selected from the reduced-dimension global STAP dictionary using the design selection criterion. Then, the optimal atoms are searched from the local STAP dictionary. Finally, these space–time steering vectors corresponding to the optimal atoms are used to construct the clutter subspace iteratively, and the STAP weight is obtained by projecting the snapshot on the subspace orthogonal to the clutter subspace. Numerical experiments with both simulated and Mountain-Top data are carried out to demonstrate the effectiveness of the proposed algorithm.

Robust STAP algorithm based on knowledge‐aided SR for airborne radar

Space–time adaptive processing (STAP) for airborne radar employs training snapshots to estimate a clutter covariance matrix (CCM). However, the resulting estimated covariance matrix can be corrupted by a target-like signal (outlier), which leads to a further target self-nulling phenomenon. When these outliers are dense, STAP performance degradation is most severe. To cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of dense outliers on target detection. This algorithm exploits the property that the clutter components of side-looking airborne radar are distributed along the clutter ridge, which is used to distinguish clutter components and outliers. Each snapshot is decomposed by the SR method, and then the similar degree function is defined to recognise and select clutter components via a threshold. Subsequently, the CCM is estimated by the select clutter components. Therefore, this algorithm can select appropriate coefficients and space–time steering vectors to assess clutter accurately. Monte Carlo experiments affirm that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms [e.g. samples matrix inverse-STAP, prolate spheroidal wave function-generalised inner product, and SR-STAP algorithms] in heterogeneous clutter environments.

Space time adaptive processing algorithm for multiple‐input–multiple‐output radar based on Nyström method

In this study, a new space-time adaptive processing (STAP) algorithm based on Nystrom (Nystrom-STAP) method is proposed to adaptively suppress the clutter and jammer in multiple-input-multiple-output (MIMO) radar system. The proposed method can reduce the number of training data and computation complexity with less performance loss. In the algorithm, by exploiting the low-rank characteristic with Nystrom extension strategy and column sampling technique, the Nystrom-based covariance estimator is obtained to approximate the clutter subspace with high accuracy, and it only need small clutter homogenous training data support. Then, combining block-diagonal feature of the jammer covariance matrix and the low-rank property of clutter subspace, the STAP filter can be formed by the inversions of low dimension matrices, which has less computation complexity. In order to further improve the convergence performance of the proposed method, with the help of adaptive displaced phase centre array technique, the reduced-rank Nystrom approaches are proposed. Simulation results demonstrate that the proposed methods exhibit better signal-to-interference-plus-noise ratio and probability of detection performance than conventional STAP algorithms with fewer training data and lower computation complexity.

Model-based clutter cancellation based on enhanced knowledge-aided parametric covariance estimation

Aerospace radar operation requires robust target detection in the presence of strong ground clutter returns. Space-time adaptive processing (STAP) is a leading approach to cull weak target signals from a strong clutter background. A scene comprised of heterogeneous clutter degrades typical STAP implementation by corrupting training data, thereby leading to covariance estimation error, corresponding adaptive clutter filter mismatch, and loss of detection performance. In the knowledge-aided parametric covariance estimation (KAPE) method, the processor estimates the parameters of a validated clutter covariance model to tailor the clutter filter response to rapid changes in heterogeneous clutter environments. Computational burden and array manifold estimation, given antenna complex gain errors, are two primary concerns when implementing KAPE. This paper develops and characterizes an enhanced KAPE (E-KAPE) approach. Specifically, we develop a computationally efficient implementation via application of Gram-Schmidt orthonormalization to the modeled clutter manifold and discuss an iterative approach to estimate complex gain errors from channel-to-channel, leading to substantial improvement in model-based clutter cancellation potential. For the specific simulation results shown, we find a 9-dB improvement in signal-to-interference-plus-noise ratio loss for our proposed approach relative to the original KAPE method. Furthermore, for these cases, we show that our E-KAPE method outperforms conventional STAP algorithms in the presence of homogeneous and heterogeneous clutter.

Range dependent clutter rejection using range-recursive space-time adaptive processing (STAP) algorithms

We here address the issue of ground clutter rejection for the detection of slowly moving targets in a non-side looking (NSL) array configuration airborne radar. The optimum space-time adaptive processing (STAP) filter needs the knowledge of the inverse of the space-time covariance matrix. In practice, it is unknown and has to be estimated. The most popular approximated method is the sample matrix inversion (SMI) method which consists in inverting the covariance matrix estimated by an average of the sample matrix over the secondary range cells. This estimator is unbiased in case of i.i.d. data. In an NSL configuration, the clutter power spectrum is range dependent and the data are consequently not i.i.d. We here present a solution to mitigate this range dependency of the data: the range recursive subspace-based algorithms. They are used in two architectures: a fully and a partially adaptive ones. Then a new range-recursive algorithm using Taylor series expansion is investigated. The performance of these algorithms are compared with that of the conventional STAP algorithms in term of SINR loss.

Fast Iterative Subspace Algorithms for Airborne STAP Radar

Space-time adaptive processing (STAP) is a crucial technique for the new generation airborne radar for Doppler spread compensation caused by the platform motion. We here propose to apply range cell snapshots-based recursive algorithms in order to reduce the computational complexity of the conventional STAP algorithms and to deal with a possible nonhomogeneity of the data samples. Subspace tracking algorithms as PAST, PASTd, OPAST, and more recently the fast approximate power iteration (FAPI) algorithm, which are time-based recursive algorithms initially introduced in spectral analysis, array processing, are good candidates. In this paper, we more precisely investigate the performance of FAPI for interference suppression in STAP radar. Extensive simulations demonstrate the outperformance of FAPI algorithm over other subspace trackers of similar computational complexity. We demonstrate also its effectiveness using measured data from the multichannel radar measurements (MCARM) program.

Circular array STAP

Traditionally, space-time adaptive processing (STAP) for airborne early warning (AEW) radar has been applied to uniform linear arrays (ULAs). However, when considering the overall radar system, electronically scanned circular arrays have advantages: a better combination of even and continual angular and temporal coverage, and mechanical simplicity because it does not need to rotate. This paper answers the question How well does STAP perform when applied to a circular array? This paper shows that for the AEW mission, circular arrays are indeed STAP compatible. However, when conventional STAP algorithms are used there may be a small loss in performance when compared with a ULA. With some care in the choice and implementation of the STAP algorithm, the majority of the degradation is at close ranges, where the target returns are relatively strong. At long ranges performance is barely affected. A STAP algorithm which compensates for the circular array environment and provides better performance than existing algorithms is presented.