CS Radar Imaging via Adaptive CAMP

In this paper we present results on application of Compressive Sensing (CS) to high resolution radar imaging and pro- pose the adaptive Complex Approximate Message Passing (CAMP) algorithm for image reconstruction. CS provides a theoretical framework that guarantees, under certain assumptions, reconstruction of sparse signals from many fewer measurements than required by the Nyquist-Shannon sampling theorem. However, whereas most conventional imaging techniques are based on linear filtering, in CS the image is obtained from a subsampled set of measurements by means of a non-linear reconstruction algorithm. A variety of such algorithms have been proposed, and, for a given problem instance, the solution will depend on a threshold that has either to be provided by the user or estimated from the com- pressed measurements. In this paper, we present an adaptive version of CAMP, where the threshold is estimated from the data itself to provide a solution with minimum reconstruction error. Our results show that the adaptive CAMP algorithm can reconstruct the image with a Mean Squared Error (MSE) comparable to the reconstruction error achieved using an optimally tuned algorithm