Two-phase Multi-target Localization Algorithm Based on Compressed Sensing

Abstract

Abstract: The RSS-based multi-target location has the natural property of the sparsity in wireless sensor networks.In this paper,a two-phase multi-target localization algorithm based on compressed sensing was proposed.This algorithm divides the grid-based target localization problem into two phases:coarse location phase and fine location phase.In the coarse location phase,the optimal number of measurements is determined according to the sequential compressedsen-sing,and then the locations of the initial candidate grids are reconstructed by l_p optimization.In the fine location phase,all candidate grids are continually divided by quadripartition method,and the accurate locations of targets in the corresponding candidate grids are estimated by using the minimum residual principle.Compared with the traditional multi-target localization algorithm using l₁ optimization,the simulation results show that the proposed localization algorithm has better localization performance when the number of targets is unknown.Meanwhile,the localization time is significantly reduced.

Key words: Compressed sensing, Multi-target location, Sequential compressed sensing, Sparse reconstruction, Wireless sensor networks

CLC Number:

TP393

LI Xiu-qin, WANG Tian-jing, BAI Guang-wei, SHEN Hang. Two-phase Multi-target Localization Algorithm Based on Compressed Sensing[J].Computer Science, 2019, 46(5): 50-56.

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