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Possibility generalized labeled multi-Bernoulli filter for multi-target tracking under epistemic uncertainty
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Cai, Han, Houssineau, Jeremie, Jones, Brandon A., Jah, Moriba and Zhang, Jingrui (2023) Possibility generalized labeled multi-Bernoulli filter for multi-target tracking under epistemic uncertainty. IEEE Transactions on Aerospace and Electronic Systems, 59 (2). pp. 1312-1326. doi:10.1109/taes.2022.3200022 ISSN 0018-9251.
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Official URL: https://doi.org/10.1109/taes.2022.3200022
Abstract
This paper presents a flexible modeling framework for multi-target tracking based on the theory of Outer Probability Measures (OPMs). The notion of labeled uncertain finite set is introduced and utilized as the basis to derive a possibilistic analog of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter, in which the uncertainty in the multi-target system is represented by possibility functions instead of probability distributions. The proposed method inherits the capability of the standard probabilistic δ-GLMB filter to yield joint state, number, and trajectory estimates of multiple appearing and disappearing targets. Beyond that, it is capable to account for epistemic uncertainty due to ignorance or partial knowledge regarding the multi-target system, e.g., the absence of complete information on dynamical model parameters (e.g., probability of detection, birth) and initial number and state of newborn targets. The features of the developed filter are demonstrated using two simulated scenarios.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Tracking (Engineering), Possibility -- Data processing, Epistemics, Uncertainty | ||||||
Journal or Publication Title: | IEEE Transactions on Aerospace and Electronic Systems | ||||||
Publisher: | IEEE | ||||||
ISSN: | 0018-9251 | ||||||
Official Date: | April 2023 | ||||||
Dates: |
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Volume: | 59 | ||||||
Number: | 2 | ||||||
Page Range: | pp. 1312-1326 | ||||||
DOI: | 10.1109/taes.2022.3200022 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2022 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 10 November 2022 | ||||||
Date of first compliant Open Access: | 10 November 2022 | ||||||
RIOXX Funder/Project Grant: |
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