Sparsity-driven distributed array imaging

We consider multi-static radar with a single transmitter and multiple, spatially distributed, linear sensor arrays, imaging an area with several targets. Assuming that the location and orientation of all the sensor arrays is known and that all measurements are synchronized, we develop compressive sensing based methods to improve imaging performance. Our approach imposes sparsity on the complex-valued reconstruction of the region of interest, with the non-zero coefficients corresponding to the imaged targets. Compared to conventional delay-and-sum approaches, which typically exhibit aliasing and ghosting artifacts due to the distributed small-aperture arrays, our sparsity-driven methods improve the imaging performance and provide high resolution. We validate our methods through numerical experiments on simulated data.