Abstract: Compared with general multi-target tracking (MTT) filters, this paper focuses on multi-target trajectory estimation 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, the 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 (BG) 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 even when the detection probability is unknown.

Key words: Trajectory poisson multi-Bernoulli; Beta-gaussian; Detection probability; Alive trajectories; All trajectories

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