Chernoff fusion of Bernoulli Gaussian max filters

TL;DR

This paper derives exact Chernoff fusion equations for Bernoulli Gaussian max filters within a possibilistic framework, enabling effective track fusion without known statistical dependencies between sources.

Contribution

It introduces a novel Chernoff fusion method for Bernoulli Gaussian max filters based on possibility theory, addressing unknown dependencies in distributed tracking.

Findings

Derived exact Chernoff fusion equations for Bernoulli Gaussian max filters.

Demonstrated the fusion scheme's effectiveness without knowledge of statistical dependence.

Utilized possibilistic approach to handle imprecise models in tracking algorithms.

Abstract

Statistical dependencies between information sources are rarely known, yet in practical distributed tracking schemes, they must be taken into account in order to prevent track divergences. Chernoff fusion is well-known and universally accepted method that can address the problem of track fusion when the statistical dependence between the fusing sources is unknown. In this paper we derive the exact Chernoff fusion equations for Bernoulli Gaussian max filters. These filters have been recently derived in the framework of possibility theory, as the analog of the Bernoulli Gaussian sum filters. The main motivation for the possibilistic approach is that it effectively deals with imprecise mathematical models (e.g. dynamics, measurements) used in tracking algorithms. The paper also demonstrates the proposed possibilistic fusion scheme in the absence of knowledge about statistical dependence.