Robust SAR STAP via Kronecker Decomposition

TL;DR

This paper introduces a Kronecker-based spatio-temporal decomposition method for improving moving target detection in SAR, significantly reducing training data needs and enhancing robustness over existing techniques.

Contribution

It proposes a novel low-rank Kronecker product covariance estimation and a separable clutter cancellation filter for SAR moving target detection.

Findings

Orders of magnitude reduction in training samples needed

Enhanced robustness to training data corruption

Confirmed advantages through simulations and real data experiments

Abstract

This paper proposes a spatio-temporal decomposition for the detection of moving targets in multiantenna SAR. As a high resolution radar imaging modality, SAR detects and localizes non-moving targets accurately, giving it an advantage over lower resolution GMTI radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used to remove the stationary clutter and enhance the moving targets. In this work, it is shown that the performance of STAP can be improved by modeling the clutter covariance as a space vs. time Kronecker product with low rank factors. Based on this model, a low-rank Kronecker product covariance estimation algorithm is proposed, and a novel separable clutter cancelation filter based on the Kronecker covariance estimate is introduced. The proposed method provides orders of magnitude reduction in the required number of training samples, as well as improved robustness to corruption of the training data. Simulation results and experiments using the Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of our approach relative to existing techniques.