Multiple‐model generalised labelled multi‐Bernoulli filter with distributed sensors for tracking manoeuvring targets using belief propagation

Abstract

AbstractThe multi‐sensor multiple‐model generalised labelled multi‐Bernoulli filter (MS‐MM‐GLMB) is presented for tracking multiple manoeuvring targets. And we develop efficient implementation for computing multi‐target posterior. The proposed implementation of the MS‐MM‐GLMB filter is composed of two main parts including centralised fusion with distributed sensors and posterior calculation using belief propagation. Specifically, the centralised fusion with distributed sensors is performed based on common predicted information to enable a full parallelisation. This fusion strategy yields the exact solution along with independence of the sensor ordering. More importantly, this fusion allows multiple sensor fusion with the MM‐GLMB filters for iterative update step for synchronous measurements. Subsequently, the belief propagation algorithm is adopted to implement the MS‐MM‐GLMB filter by efficiently calculating the marginal posterior distributions of GLMB form with superior tracking performance. The belief propagation solution to solving data association is presented for the computationally efficient centralised tracking of the manoeuvring targets. Compared with Gibbs‐based implementation, the proposed belief‐propagation‐based method performs well or better, especially in more challenging scenario.