Maximum Lilkelihood Source Localization in Wireless Sensor Network Using Particle Swarm Optimization

Abstract

Wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and other applications. Source localization is one of the important problem in wireless sensor network. In literature a decentralized approach using strong antena arrays at each node or sensor arrays at different positions are used to localize the sources. In this paper a purely co-operative method where every node will participate in estimation. The network does the bearing estimation by optimizing maximum likelihood function by forming random array among all the nodes. Particle swarm optimization is used to optimize ML function because it is more efficient compared to other evolutionary algorithm like GA. Finally the results are compared with most analyzed MUSIC algorithm.