Joint Estimation of NLOS Building Layout and Targets via Sparsity-Driven Approach
Jiahui Chen, Yang Zhang, Shisheng Guo, Guolong Cui, Peilun Wu, Chao Jia, Lingjiang Kong
Abstract
Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, the NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this paper, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets NLOS scenario for the multiple-input-multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape-remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared to the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results.