An adaptive sparse subsampling matrix design strategy for compressive sensing SAR

Abstract

With the rapid development and demanding requirement of high resolution and wide swath synthetic aperture radar (SAR), the volume of data acquisition becomes increasingly large as well as higher hardware complexity. Compressive Sensing (CS) theory, as an effective and accurate signal reconstruction technique, employs an extremely smaller set of measurements than what is typically considered necessary by Nyquist-Shannon sampling theorem. In this paper, an adaptive sparse subsampling matrix design strategy is presented and analyzed. By utilizing this adaptive measurement matrix strategy, not only is the signal recovery exact, but also the storage requirement of subsampling matrix and the computational complexity of generating linear measurement vector are significantly reduced, so that large-scale SAR signal recovery is spatially and temporally feasible. The validity of the proposed strategy is verified by sparse scene simulation results with multi-point targets.