Compressive sensing multi-target diffusive source localisation using sparse recovery algorithms in sensor networks. (23rd January 2021)

• Record Type: Journal Article
• Authors: Zhang, Yong; Yan, Zhi; Chen, Qi; Fei, Teng; Zhang, Liyi
• Is Part Of: International journal of sensor networks. Volume 35:Number 1(2021)
• Page Start: 32
• Page End: 41
• Publication Date: 2021-01-23
• Subjects: sensor network -- compressive sensing -- variational Bayesian expectation maximisation -- diffusive source localisation; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

Abstract

According to the multi-target diffusive source localisation in sensor networks, a compressive sensing sparse recovery algorithm was proposed for the mismatching problem of the target sources sparsity and the high-dimensional redundant sampling signals. Firstly, the compressive sensing system model and the related terms were given and explained. Then, the joint optimal estimation of the sparse diffusive source vector and the diffusion distribution state were realised with the variational Bayesian expectation maximisation algorithm (VB-EM). In which, the dynamic compressive sensing dictionary model of the real target source sparse representation was designed and adjusted with the grid division parameters optimisation for the dictionary mismatch problem solving. Finally, the simulation results show that the proposed compressive sensing method with VB-EM algorithm could effectively achieve the diffusive source parameters estimation and its diffusion distribution state prediction. Compared with the traditional compressive sensing sparse recovery algorithms, it could obtain higher robustness performance for the rapid and accurate localisation in complex environment.