Abstract
With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.
Original language | English |
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Article number | 487978 |
Journal | International Journal of Distributed Sensor Networks |
Volume | 2015 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
ASJC Scopus subject areas
- General Engineering
- Computer Networks and Communications