A Novel TPMBM Filter for Partly Resolvable Multitarget Tracking

Due to the limitation of sensor resolution, multiple target locations in close proximity can result in merged measurements, which makes it difficult for traditional measurement association assumptions to be met and makes the filter unable to resolve single target trajectories. In this article, we use random finite set (RFS) theory to develop a novel trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for partly resolvable multitarget (PRM) tracking, which is called the TPMBM-PRMT filter. First, we improve the likelihood model for the unresolved measurements and use the breadth-first search (BFS) algorithm to partition the predicted set of detected trajectories. In addition, a trajectory subset-measurement association update method is proposed, where all trajectories within the subset share the same measurement, and a trajectory Poisson multi-Bernoulli (TPMB) approximation is designed to generate multiple high-quality association hypotheses and ensure the feasibility of the filter. Finally, the Gaussian L-scan approximation is used in the filter implementation, which reduces the computational cost while ensuring the filtering efficiency. Simulation results show that the filter in this article can effectively handle the merging of multitarget measurements and achieve stable tracking of PRM.