Multiple Target Localization Based on Binary Salp Swarm Algorithm optimized Compressive Sensing Reconstntction under WSNs

In this paper, a multiple target localization algorithm based on compressive sensing reconstruction of Binary Salp Swarm Algorithm (BSSA) is proposed to improve the multi-target positioning accuracy and anti-noise in wireless sensor networks. The continuous salp swarm algorithm is discretized in the...

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Bibliographic Details
Published in:Chinese Automation Congress (Online) pp. 344 - 349
Main Authors: Ji, Zhangsheng, Xiao, Benxian
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2019
Subjects:
binary salp swarm algorithm
Clustering algorithms
Compressed sensing
foraging
Mathematical model
multiple target localization
Optimization
rapid coordination change
Sociology
Wireless sensor networks
ISSN:2688-0938
Online Access:Get full text
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Summary:In this paper, a multiple target localization algorithm based on compressive sensing reconstruction of Binary Salp Swarm Algorithm (BSSA) is proposed to improve the multi-target positioning accuracy and anti-noise in wireless sensor networks. The continuous salp swarm algorithm is discretized in the binary space, and the essential characteristics of the rapid coordination change and foraging of the salp swarm are preserved, and then used for the reconstruction of compressive sensing signals to achieve multi-target positioning under the wireless sensor networks. The experimental results shows that compared with the traditional compressive sensing reconstruction algorithm, the algorithm has good noise immunity and counting performance. The positioning performance is better than the greedy matching pursuit(GMP) algorithm and the traditional l 1 -norm minimization algorithm.
ISSN:2688-0938
DOI:10.1109/CAC48633.2019.8996231