JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Trajectory Poisson multi-Bernoulli filters with unknown detection probability

Author(s): Xiangfei ZHENG¹, Kaidi LIU¹, Hongwei LI^‡¹
Affiliation(s): ¹School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
Corresponding email(s): hwli@cug.edu.cn
Key Words: Trajectory Poisson multi-Bernoulli; Beta-Gaussian; Detection probability; Alive trajectories; All trajectories

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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,in press.https://doi.org/10.1631/FITEE.2400606

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author="Xiangfei ZHENG¹, Kaidi LIU¹, Hongwei LI^‡¹",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400606"
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%A Kaidi LIU¹
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doi="https://doi.org/10.1631/FITEE.2400606"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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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