Measurement‐Driven Group Target Poisson Multi‐Bernoulli Mixture Filter with Adaptive Birth

ABSTRACT In the application of group‐target overall tracking, it is necessary to simultaneously track the positions, measurement rates and extended shape states of the group targets. Among existing studies, the gamma Gaussian inverse Wishart (GGIW) distribution is the prevailing model for describing the state of group targets. The PMBM filter, with its rigorous mathematical framework, high‐precision tracking performance and ease of implementation, has been widely applied to group‐target overall tracking. However, in actual group target tracking, the PMBM filter requires the assumption of new group targets. Inaccurate prior information about the group targets may make it impossible to track them. To address this issue, this paper proposes a measurement‐driven adaptive birth PMBM filter. In this filtering framework, the group target parameter estimation based on single‐frame measurements is achieved through the proposed two‐layer nested expectation‐maximisation (EM) GGIW parameter estimation method. The inner EM algorithm realises the maximum posterior of GGIW parameters under the given group GGIW parameter prior, whereas the outer EM algorithm estimates the optimal GGIW parameter prior. During implementation, both serial and parallel methods are presented. The effectiveness of the proposed PMBM filter is verified through simulation experiments.