Probabilistic Self-Localization for Sensor Networks

Abstract:

This paper describes a technique for the probabilistic 
self-localization of a sensor network based on noisy 
inter-sensor range data.  Our method is based on a number 
of parallel instances of Markov Chain Monte Carlo (MCMC). 
By combining estimates drawn from these parallel chains, 
we build up a representation of the underlying probability 
distribution function (PDF) of the network pose.  Our 
approach includes sensor data incrementally in order to 
avoid local minima and is shown to produce meaningful 
results efficiently.  We return a distribution over sensor 
locations rather than a single maximum likelihood 
estimate.  This can then be used for subsequent 
exploration and validation.