Distributed track‐to‐track fusion for non‐linear systems with Gaussian mixture noise
Kelin Lu, Changyin Sun, Qien Fu, Qian Zhu · IET Radar Sonar & Navigation · 2019
The authors propose a distributed state estimation algorithm based on optimal track‐to‐track fusion for local posteriors in terms of Gaussian mixtures. The track‐to‐track fusion system is implemented with both parallel and sequential structure based on generalised covariance intersection rule. They obtain the optimal fusion coefficients in a computationally efficient manner via Monte Carlo importance sampling method. The Dirac mixture approximation method is proposed for the computation of arbitrary power of a Gaussian mixture density. The resulting Gaussian mixture fusion rule is analytical and applicable to the multi‐sensor case. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with existing track‐to‐track fusion algorithms.