Kronecker STAP and SAR GMTI

TL;DR

This paper introduces a GPU-accelerated Kronecker STAP algorithm for large SAR datasets and extends it to multi-pass data, improving moving target detection in high-resolution radar imaging.

Contribution

It presents a massively parallel GPU implementation of Kronecker STAP and extends the method to multi-pass data for enhanced moving target detection.

Findings

GPU implementation enables processing of large SAR datasets.

Multi-pass extension improves detection accuracy.

Kronecker modeling reduces training sample requirements.

Abstract

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 on multiantenna SAR to remove the stationary clutter and enhance the moving targets. In (Greenewald et al., 2016) it was 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, providing robustness and reducing the number of training samples required. In this work, we present a massively parallel algorithm for implementing Kronecker product STAP, enabling application to very large SAR datasets (such as the 2006 Gotcha data collection) using GPUs. Finally, we develop an extension of Kronecker STAP that uses information from multiple passes to improve moving target detection.