Fusion of Information for Sensor Self-Localization by a Monte Carlo Method

We propose a distributed algorithm for sensor localization using beacon nodes. In this algorithm, beacon nodes broadcast distributions which contain information about their location. Nearby sensor nodes with unknown location information use this transmitted information and received beacon signal characteristics to estimate their positions. Sensors that estimate their positions become new beacons. A Monte Carlo method known as Importance Sampling is used for fusing these distributions and for obtaining approximations of the posterior distributions of the sensor locations. We also compute the Bayesian Cramér-Rao bounds for self-localization of sensors and study the impact of the beacons' prior location information and other system parameters. We analyze the performance of the proposed algorithm through computer simulations and compare it with numerically obtained bounds.