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Trajectory Poisson multi-Bernoulli filters with unknown detection probability
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Xiangfei ZHENG1, Kaidi LIU1, Hongwei LI‡1. Trajectory Poisson multi-Bernoulli filters with unknown detection probability[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Trajectory Poisson multi-Bernoulli filters with unknown detection probability",
author="Xiangfei ZHENG1, Kaidi LIU1, Hongwei LI‡1",
journal="Frontiers of Information Technology & Electronic Engineering",
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number="-1",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400606"
}
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DOI - 10.1631/FITEE.2400606
Abstract: Compared with general multi-target (MTT) tracking 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.
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