Interrupted SAR persistent surveillance via group sparse reconstruction of multipass data

In this paper we present a new method for synthetic aperture radar (SAR) image formation from interrupted, multipass SAR phase history data, with application to persistent surveillance SAR imaging. We propose a new compressed sensing-motivated approach to reconstruction that jointly processes multipass interrupted data using a sparse recovery technique with a group support constraint and results in improved imagery. We compare our approach, a group sparsity (GS) algorithm, to methods that independently process each data pass, namely the basis pursuit denoising and iterative adaptive approach methods. We find that the joint processing of GS results in coherent change detection gains over the other approaches regardless of interrupt pattern. To illustrate the capabilities of GS, we evaluate coherent change detection performance using images from the Gotcha SAR.