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.
Highlights
With the advances of wireless communications, sensor techniques, and microelectromechanical system (MEMS), wireless sensor network has attracted more and more attention and research in recent years
We propose a self-adaptive particle swarm optimization based multiple source localization method (SAPSO-MSL)
A trust index based subtract on negative add on positive (TISNAP) method [19] is developed for multiple source localization in binary sensor network
Summary
With the advances of wireless communications, sensor techniques, and microelectromechanical system (MEMS), wireless sensor network has attracted more and more attention and research in recent years. The binary sensor node only transmits “1” if the target is detected; otherwise it keeps silence It is a low power consumption and bandwidth-efficient solution for target localization in wireless sensor network. Several methods have been employed to estimate the target location, received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) in wireless sensor network [2]. TDOA method locates target by measuring the signals’ arrival time difference between sensor nodes. We investigate the multiple targets localization based on RSS method. We propose a self-adaptive particle swarm optimization based multiple source localization method (SAPSO-MSL). (2) The self-adaptive particle swarm optimization which owns low computational complexity is used to estimate the location of source.
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