  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.
