A Consistent Track-to-Track Fusion Method Based on Copula Theory

Abstract EN

This paper addresses the problem of distributed fusion when the conditional independence assumptions on sensor measurements or local estimates are not met.

A new data fusion algorithm called Copula fusion is presented.

The proposed method is grounded on Copula statistical modeling and Bayesian analysis.

The primary advantage of the Copula-based methodology is that it could reveal the unknown correlation that allows one to build joint probability distributions with potentially arbitrary underlying marginals and a desired intermodal dependence.

The proposed fusion algorithm requires no a priori knowledge of communications patterns or network connectivity.

The simulation results show that the Copula fusion brings a consistent estimate for a wide range of process noises.