A sparsity based approach to velocity SAR imaging

We recently successfully developed an airborne MSAR (Multichannel Synthetic Aperture Radar) test bed system that consists of 32 along-track phase centers through the use of two transmit horns and 16 receive antennas [1-4]. We have subsequently deployed this system, both in September 2014 and more recently in October 2015, to perform extensive and systematic data collections on a variety of land-based and maritime targets under different environmental conditions. The resulting data poses important signal processing challenges pertaining to optimum ways of combining the signals obtained from various channels so that the underlying information of interest can be effectively extracted in the presence of noise and clutter. In this paper we focus on the imaging problem and propose a novel method of simultaneously exploiting the multichannel structure of the data acquisition and the underlying sparse structure of the scene being imaged. After giving a brief overview of our airborne NRL MSAR system and the basics of velocity processing, we proceed to describe our novel algorithm and demonstrate our initial experimental results. The novelty of this paper is two-fold: to the best of our knowledge, this is first time that velocity processing has been used in conjunction with sparsity based processing; and that the resulting approach is applied to real data captured by our airborne NRL MSAR system.