Particle swarm optimisation based on Monte Carlo localisation for mobile sensor network
by Chuan Xin Zhao; Ru Chuan Wang
International Journal of Modelling, Identification and Control (IJMIC), Vol. 14, No. 4, 2011

Abstract: Monte Carlo localisation algorithms have been proposed for mobile sensor networks which indicate a new direction for localisation in sensor networks. In this article, we propose an improved Monte Carlo localisation algorithm based on particle swarm optimisation in order to solve the problem of sample scarcity for localisation. In the new scheme, we draw samples from box which constructed by neighbour anchors' radio ranges overlap, then use particle swarm optimisation to produce better samples by incorporating the newest measurement. Hybrid method reduces resample process and improves location accuracy. Simulation results show that proposed localisation is more accurate and effective for most condition than Monte Carlo location and Monte Carlo localisation boxed, MCB.

Online publication date: Sat, 21-Mar-2015

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