Wall mitigation using discrete prolate spheroidal sequences for sparse indoor image reconstruction

Detection and localization of stationary targets behind walls is primarily challenged by the presence of the overwhelming EM signature of the front wall in the radar returns. In this paper, we address suppression of front wall clutter prior to image reconstruction for a stepped-frequency radar imaging system, when different sets of few frequency observations are available at different antennas in physical or synthetic aperture arrays. We use a dictionary based on discrete prolate spheroidal sequences to represent the wall return and employ a block sparse model to capture and subsequently remove the wall clutter at each available antenna. The proposed scheme enables sparsity-based image reconstruction techniques to effectively detect and localize behind-the-wall targets from reduced data measurements.