Robust SAR STAP via Kronecker Decomposition
This addresses the challenge of moving target detection in SAR for radar imaging applications, offering a robust and efficient solution with incremental improvements over existing STAP methods.
The paper tackles moving target detection in multiantenna SAR by proposing a spatio-temporal decomposition that models clutter covariance as a low-rank Kronecker product, resulting in orders of magnitude reduction in required training samples and improved robustness to data corruption.
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.