Fast range-based localization of targets using particle swarm optimization

Abstract

Target localization is an active area of research which has several applications in the fields of robotics, defense and geology. In this paper, our goal is to localize a target based on range measurements obtained using a network of sensors scattered in the 3D continuum. To this end, we make use of the biologically inspired particle swarm optimization (PSO) algorithm. In this context, we propose a novel modification of PSO algorithm that leads to faster convergence, and eliminates the flip ambiguity encountered by coplanar sensors. Our initial results over several simulation runs highlight the accuracy and speed of the proposed approach. This paper also proposes a statistical approach to optimally place a given set of sensors such that the localization error is minimized over certain trajectories of the target. The optimal locations of the sensors are estimated using the Cramer-Rao lower bound (CRLB) as the cost function.