Autofocus Bayesian compressive sensing for multipath exploitation in urban sensing

Exploitation of group sparsity under multipath propagation enables high-resolution ghost-free imaging in urban sensing and through-the-wall radar imaging applications. Multipath exploitation schemes typically require exact prior information of the indoor scene layout and transceiver locations to eliminate ghosts targets. Imperfections in the prior knowledge lead to performance degradation of such schemes. In this paper, a novel autofocus Bayesian compressive sensing approach is proposed for joint scene reconstruction and correction of phase errors resulted from transceiver position uncertainties. Supporting simulation results are provided to demonstrate the effectiveness of the proposed algorithm.