Title: Compressive sensing multi-target diffusive source localisation using sparse recovery algorithms in sensor networks

Authors: Yong Zhang; Zhi Yan; Qi Chen; Teng Fei; Liyi Zhang

Addresses: Information Engineering College, Tianjin University of Commerce, Tianjin, 300134, China ' Information Engineering College, Tianjin University of Commerce, Tianjin, 300134, China ' Information Engineering College, Tianjin University of Commerce, Tianjin, 300134, China ' Information Engineering College, Tianjin University of Commerce, Tianjin, 300134, China ' Information Engineering College, Tianjin University of Commerce, Tianjin, 300134, China

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.

Keywords: sensor network; compressive sensing; variational Bayesian expectation maximisation; diffusive source localisation.

DOI: 10.1504/IJSNET.2021.112887

International Journal of Sensor Networks, 2021 Vol.35 No.1, pp.32 - 41

Received: 14 May 2020
Accepted: 24 May 2020
Published online: 08 Feb 2021 *

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