Robust multipath exploitation radar imaging in urban sensing based on Bayesian compressive sensing

In through-the-wall radar imaging applications, exploitation of group sparsity of the targets under multipath propagation allows high-resolution ghost-free imaging. However, such multipath exploitation schemes may suffer from imperfect knowledge of the surrounding scatterers, such as interior walls. In this paper, a novel two-stage Bayesian compressive sensing approach is proposed for joint scene reconstruction and wall location estimation. The proposed method is capable of not only acquiring enhanced images by exploiting multipath propagation, but also estimating wall locations with high accuracy. In addition, compared to computationally demanding genetic approaches, the proposed method achieves robust imaging with a low complexity.