Space-time Adaptive Processing Detectors Research Articles

Robust STAP Detection Based on Volume Cross-Correlation Function in Heterogeneous Environments

The performance of moving target detection in heterogeneous environments with the traditional space-time adaptive processing (STAP) may degrade when the real clutter environments deviate from the prior assumption on the clutter distribution. In this letter, a new detector for STAP applications based on volume cross-correlation function (VCF), namely VCF-STAP, is proposed to achieve robust performance of moving target detection in heterogeneous environments. In the new VCF-STAP, the VCF is used to form a distance measure between the sample signal subspace and the target subspace without modeling the clutter distribution. Then, a new robust STAP detection statistic is constructed using this distance measure. Simulation and experimental results show that the proposed VCF-STAP achieves robust performance of moving target detection in heterogeneous environments, especially it achieves much superior detection performance compared with existing STAP methods when the real clutter environments do not satisfy their prior assumptions. Besides, it is also shown that VCF-STAP has the constant false alarm rate (CFAR) property.

Robust waveform design for MIMO–OFDM‐based STAP in the presence of target uncertainty

The issue of robust orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar waveform design is investigated here with target uncertainty to enhance the worst-case space-time adaptive processing (STAP) detection performance of MIMO–OFDM radar. Based on the max–min method, the robust waveform optimisation problem is constructed under the criterion of maximising the worst-case output SINR, namely, signal–interference–noise ratio, with constraints of constant modulus and target estimation errors to enhance the robustness of STAP's detection performance to target uncertainty. To handle the obtained non-linear and complicated issue, an approach based on diagonal loading (DL) is presented. By employing DL method, the optimisation issue can be relaxed to semidefinite programming one, and thus it can be tackled very efficiently. When compared with uncorrelated signals, the state-of-the-art robust and non-robust waveform design methods for MIMO–STAP, numerical simulations reveal that the developed approach can enhance the robustness of the detection performance of STAP significantly.

Minimum Mean Square Distance Estimation of Subspaces in presence of Gaussian sources with application to STAP detection

We consider the problem of signal subspace estimation from a given sample set. We rely on the Bayesian framework developped in [1] to obtain minimum mean square distance (MMSD) estimators, which minimize the expected distance between the true projection matrix UUT and its estimate ÛÛT. In this work, we extend the estimators of [1] to the context of linear model with Gaussian sources, with respectively Bingham and von Mises Fisher priors for the basis Ū. Numerical simulations are given in order to assess the performance of the proposed estimator. The interest of the considered approach is finally illustrated on a real data set, for a Space Time Adaptive Processing (STAP) application.

Parametric space–time detection and range estimation of a small target

In this study, the authors deal with the problem of parametric detection for relatively small targets using space–time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.

Generalized Robust Shrinkage Estimator and Its Application to STAP Detection Problem

Recently, in the context of covariance matrix estimation, in order to improve as well as to regularize the performance of the Tyler's estimator [1] also called the Fixed-Point Estimator (FPE) [2], a "shrinkage" fixed-point estimator has been introduced in [3]. First, this work extends the results of [3,4] by giving the general solution of the "shrinkage" fixed-point algorithm. Secondly, by analyzing this solution, called the generalized robust shrinkage estimator, we prove that this solution converges to a unique solution when the shrinkage parameter $\beta$ (losing factor) tends to 0. This solution is exactly the FPE with the trace of its inverse equal to the dimension of the problem. This general result allows one to give another interpretation of the FPE and more generally, on the Maximum Likelihood approach for covariance matrix estimation when constraints are added. Then, some simulations illustrate our theoretical results as well as the way to choose an optimal shrinkage factor. Finally, this work is applied to a Space-Time Adaptive Processing (STAP) detection problem on real STAP data.

Conjugate Gradient Parametric Detection of Multichannel Signals

The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is reexamined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as reduced training and computational requirements as compared with fully adaptive STAP detectors, the PAMF detector as a reduced-dimensional solution remains unclear. This paper employs the conjugate-gradient (CG) algorithm to solve the linear prediction problem arising in the PAMF detector. It is shown that CG yields not only a new computationally efficient implementation of the PAMF detector, a new and efficient AR model order selection method that can naturally be integrated with CG iterations, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. We first consider the integration of the CG algorithm with the matched filter (MF) and parametric matched filter (PMF) when the covariance matrix of the disturbance signal is known. It is then extended to the adaptive case where the covariance matrix is estimated from training data. Important issues such as computational complexity and convergence rate are discussed. Performance of the proposed CG-PAMF detector is examined by using the KASSPER and other computer generated data.

Importance sampling for characterizing STAP detectors

This paper describes the development of adaptive importance sampling (IS) techniques for estimating false alarm probabilities of detectors that use space-time adaptive processing (STAP) algorithms. Fast simulation using IS methods has been notably successful in the study of conventional constant false alarm rate (CFAR) radar detectors, and in several other applications. The principal objectives here are to examine the viability of using these methods for STAP detectors, develop them into powerful analysis and design algorithms and, in the long term, use them for synthesizing novel detection structures. The adaptive matched filter (AMF) detector has been analyzed successfully using fast simulation. Of two biasing methods considered, one is implemented and shown to yield good results. The important problem of detector threshold determination is also addressed, with matching outcome. As an illustration of the power of these methods, two variants of the square-law AMF detector that are thought to be robust under heterogeneous clutter conditions have also been successfully investigated. These are the envelope-law and geometric-mean STAP detectors. Their CFAR property is established and performance evaluated. It turns out the variants have detection performances better than those of the AMF detector for training data contaminated by interferers. In summary, the work reported here paves the way for development of advanced estimation techniques that can facilitate design of powerful and robust detection algorithms

Demonstration of knowledge-aided space-time adaptive processing using measured airborne data

The design and analysis of a knowledge-aided detector for airborne space-time adaptive processing (STAP) applications are addressed. The proposed processor is composed of a training data selector, which chooses secondary cells best representing the clutter statistics in the cell under test, and an adaptive processor for detection processing. The data selector is a hybrid algorithm, which pre-screens training data through the use of terrain information from the United States Geological Survey. Then, in the second stage, a data-driven selector attempts to eliminate residual non-homogeneities. The performance of this new approach is analysed using measured airborne radar data, obtained from the multi-channel airborne radar measurements program, and is compared with alternative STAP detectors proposed in the open literature.