  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.
