Bayesian Localization in Sensor Networks: Distributed Algorithm and Fundamental Limits

Self-localization in ad-hoc sensor networks is becoming a crucial issue for several location-aware applications. This technology implies the combination of absolute anchor locations with relative inter-node information exchanged on a peer-to-peer basis. In this paper we investigate a distributed algorithm and fundamental performance bounds for Bayesian cooperative localization in stochastic networks. Nodes are assumed to be randomly deployed within a finite space according to a prior distribution. Bayesian inference is performed through an iterative local message passing procedure based on belief propagation and particle-filtering message representation. The algorithm performance is analyzed for a simplified scenario in which unknown node positions are randomly scattered along a line segment and anchors are fixed. Global Cramer-Rao bounds are derived and compared to the performance of the distributed algorithm.