Multi-sensor multi-target tracking with generalized labeled multi-Bernoulli filter based on track-before-detect observation model using Gaussian belief propagation

Abstract

Tracking an unknown number of low-observable targets is notoriously challenging problem. This paper extends the multi-sensor multi-target tracking with generalized labeled multi-Bernoulli (GLMB) filter into the track-before-detect (TBD) observation model and presents its efficient implementation. The Gaussian belief propagation (BP) is applied into multi-sensor GLMB-TBD filter to address the computational bottleneck of computing GLMB filtering density for tracking multiple targets in challenging scenario. The Gaussian BP algorithm is used to directly compute marginal densities from the joint posterior density through the BP belief update wherein the GLMB filtering density at given time serves as prior information for next recursion. Compared to the Gibbs sampling-version implementation, the Gaussian BP-version implementation avoids the pruning of GLMB components without association information loss and the relevant association is preserved for BP belief update. Hence, the proposed Gaussian BP-version implementation can improve the tracking accuracy. Moreover, the computational complexity of our proposed Gaussian BP-version implementation scales linear complexity in the total number of measurements from all sensors. More importantly, the linear scaling in number of Bernoulli components for our proposed Gaussian BP-version implementation improves on the quadratic complexity exhibited by the Gibbs-version one. The experiment results show that the computational cost of the Gaussian BP-version implementation is significantly less than that of the Gibbs sampling-version implementation for the multi-sensor GLMB-TBD filter.