  In this work the detection of moving targets in multiantenna SAR is
considered. As a high resolution radar imaging modality, SAR detects and
identifies stationary targets very well, giving it an advantage over classical
GMTI radars. Moving target detection is more challenging due to the "burying"
of moving targets in the clutter and is often achieved using space-time
adaptive processing (STAP) (based on learning filters from the spatio-temporal
clutter covariance) to remove the stationary clutter and enhance the moving
targets. In this work, it is noted that in addition to the oft noted low rank
structure, the clutter covariance is also naturally in the form of a space vs
time Kronecker product with low rank factors. A low-rank KronPCA covariance
estimation algorithm is proposed to exploit this structure, and a separable
clutter cancelation filter based on the Kronecker covariance estimate is
proposed. Together, these provide orders of magnitude reduction in the number
of training samples required, as well as improved robustness to corruption of
the training data, e.g. due to outliers and moving targets. Theoretical
properties of the proposed estimation algorithm are derived and the significant
reductions in training complexity are established under the spherically
invariant random vector model (SIRV). Finally, an extension of this approach
incorporating multipass data (change detection) is presented. Simulation
results and experiments using the real Gotcha SAR GMTI challenge dataset are
presented that confirm the advantages of our approach relative to existing
techniques.
