Sequential Monte Carlo methods for localization in wireless networks

Abstract

Wireless indoor and outdoor localization systems received a great deal of attention in recent years. This chapter surveys first the current state-of-the-art of localization techniques. Next, it formulates the problem of localization within Bayesian framework and presents sequential Monte Carlo methods for localization based on received signal strength indicators (RSSIs). Multiple model particle filters are developed and their performance is evaluated with RSSIs by accounting for and without considering the measurement noise time correlation. A Gibbs sampling algorithm is presented for estimating the unknown parameters of the measurement noise which highly increases the accuracy of the localization process. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi-model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localization accuracy is demonstrated. © 2013 Springer-Verlag Berlin Heidelberg.