Distributed multi‐sensor fusion using generalised multi‐Bernoulli densities
Wei Yi, Meng Jiang, Reza Hoseinnezhad, Bailu Wang · IET Radar Sonar & Navigation · 2016
A new method for distributed multi‐target tracking with multistatic radar systems is presented.The proposed method is based on using generalised covariance intersection (GCI) of multi‐object densities for fusion of the posteriors within a multi‐object Bayesian filtering scheme. The presented solution is particularly formulated for sensor fusion with posterior densities that are parameterised as generalised multi‐Bernoulli (GMB) distributions which are the unlabelled version of VoCVo densities by discarding the labels. To obtain a closed‐form solution for fusing GMB densities, the authors use an efficient approximation to the densities. The approximated density is another GMB density that preserves both the first‐order moment (intensity or PHD) and the cardinality distribution of the original density. As such, it is called the second‐order approximation of the GMB (SO‐GMB) density. The resulting explicit expressions for the GCI fusion using SO‐GMB approximations allow distributed sensor fusion, not only with VoCVo filters, but also with M‐generalised labelled multi‐Bernoulli and labelled multi‐Bernoulli filters being in place as local filters in the multistatic radar system. In two challenging multi‐target tracking scenarios, the tracking performance of the proposed method is shown to outperform the state of art.