PMBM-based multi-target tracking under measurement merging

Abstract

Most multi-target tracking (MTT) algorithms have been developed based on the assumption that a measurement can be produced by at most one target. In contrast, the situation that multiple targets produce a single measurement has been addressed relatively rarely. Such a problem arises in the application where targets are close to each other in the measurement space and thus occupy the same resolution bin. To handle such a problem, this paper proposes a Poisson multi-Bernoulli mixture (PMBM) based approach to perform MTT under measurement merging. The proposed method shares the same prediction step as the standard PMBM filter. However, the PMBM density is not a conjugate prior to the multi-target likelihood with merged measurements, and an approximate PMBM density to the posterior is obtained. The Gaussian mixture based implementation strategy is introduced in this paper, and the Gibbs sampling strategy is further exploited to implement the proposed method efficiently. The performance of proposed method is verified via simulations.