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Compared with general multi-target tracking filters, this paper focuses on multi-target trajectories in scenarios where the detection probability of the sensor is unknown. In this paper, two trajectory Poisson multi-Bernoulli (TPMB) filters with unknown detection probability are proposed: one for alive trajectories and the other for all trajectories. First, an augmented trajectory state with detection probability is constructed, and then two new state transition models and a new measurement model are proposed. Then, this paper derives the recursion of TPMB filters with unknown detection probability. Furthermore, the detailed beta-Gaussian implementations of TPMB filters for alive trajectories and all trajectories are presented. Finally, simulation results demonstrate that the proposed TPMB filters with unknown detection probability can achieve robust tracking performance and effectively estimate multi-target trajectories.
Zheng et al. (Sat,) studied this question.