Parametric dictionary learning for TWRI using distributed particle swarm optimization

This paper considers a distributed network of through-the-wall radars for accurate indoor scene reconstruction in the presence of multipath propagation. A sparsity based method is proposed for eliminating ghost targets under imperfect knowledge of interior wall locations. Instead of aggregating and processing the observations at a central fusion station, joint scene reconstruction and estimation of interior wall locations is carried out in a distributed manner across the network. More specifically, an alternating minimization approach is utilized to solve the associated non-convex optimization problem, wherein the sparse scene is reconstructed using the recently proposed modified distributed orthogonal matching pursuit algorithm while the wall location estimates are obtained with a novel distributed particle swarm optimization algorithm (D-PSO) proposed in this paper. Existing literature on averaging consensus is leveraged to derive the D-PSO algorithm. The efficacy of proposed approach is demonstrated using numerical simulation.