An Empirical Study into the Use of Chernoff Information for Robust, Distributed Fusion of Gaussian Mixture Models

Abstract

This paper considers the problem of developing algorithms for the distributed fusion of Gaussian mixture models through the use of Chernoff information. We derive a first order approximation and show that, in a distributed tracking problem in which sensor nodes are equipped with only range-only or bearing-only sensors, it yields consistent estimates