Acoustic sensor network node self-localization based on adaptive particle swarm optimization

Authors:
Jinjie Yao;Yan Han;Liming Wang;Jinxiao Pan;Peirui Bai;Jianhui Zhou
Affiliations:
National Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan, China;National Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan, China;National Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan, China;National Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan, China;College of Information & Electrical Engineering, Shandong University of Science and Technology, Qingdao, China;Sichuan of Institute Aerospace Electronic Equipment, Chengdu, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

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Abstract

It is quite important to obtain the sensor nodes location information in the underwater acoustic sensor networks localization. A method of acoustic sensor network node self-localization based on adaptive particle swarm optimization is proposed aiming at the stringent difficulties of the underwater acoustic sensor node localization and the shortage of standard particle swarm optimization (PSO) algorithm which is easily trapped into the local optimum. In the method, the global search ability and the local performance of the PSO algorithm are effectively improved by balancing the stochastic inertia weight. At the same time, the proposed method finds easy and elegant solutions to get rid of the local optimization by adopting the adaptive mutation strategy. The experimental results indicated that the new method can effectively solve the current problem in the underwater acoustic sensor node localization, and the pointing accuracy achieves 0.605m.