Robust SAR STAP via Kronecker decomposition (original) (raw)

2016, IEEE Transactions on Aerospace and Electronic Systems

Canonical framework for describing suboptimum radar space-time adaptive processing (STAP) techniques

We address the problem of detecting slow moving targets from a moving radar system using space-time adaptive processing (STAP) techniques. Optimum interference rejection is known to require the estimation and the subsequent inversion of an interference-plus-noise covariance matrix. To reduce the number of training samples involved in the estimation and the computational cost inherent to the inversion, many suboptimum STAP techniques have been proposed. Earlier attempts at unifying these techniques had a limited scope. In this paper, we propose a new canonical framework that unifies all of the STAP methods we are aware of. This framework can also be generalized to include the estimation of the covariance matrix and the compensation of the range dependence; it applies to monostatic and bistatic configurations. We also propose a new decomposition of the CSNR performance metric that can be used to understand the performance degradation specifically due to the use of a suboptimum method.

Overview of Space-Time Adaptive Processing Algorithms for Radar Systems

Radar systems are confronted with increasingly complex objectives in a highly non cooperative interference environment. To meet the challenge, sensor systems are forced to utilize multidimensional signal processing techniques. In fact, conventional signal processing perform poorly due to lack of knowledge of highly dimensional statistic requirements. On the other hand, traditional adaptive signal processing techniques break down or have suboptimal performance in these cases. A possible approach is optimal or suboptimal Space-Time Adaptive Processing (STAP) techniques. This article presents an introduction to STAP which was originally developped for detecting slow moving objectives from airborne radars. STAP is a data domain implementation of an optimal filter solution. The optimum filter is designed based on the known covariance matrix and the known Doppler-angle

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