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A linear algorithm for multi-target tracking in the context of possibility theory

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Houssineau, Jeremie (2021) A linear algorithm for multi-target tracking in the context of possibility theory. IEEE Transactions on Signal Processing, 69 . pp. 2740-2751. doi:10.1109/TSP.2021.3077304 ISSN 1053-587X.

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Official URL: http://dx.doi.org/10.1109/TSP.2021.3077304

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Abstract

We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-tracking practitioner must deal with when working with real data. We also introduce and study variants of the notions of point process and intensity function, which lead to the derivation of an analogue of the probability hypothesis density (PHD) filter. The gains provided by the considered modelling framework in terms of flexibility lead to the loss of some of the abilities that the PHD filter possesses; in particular the estimation of the number of targets by integration of the intensity function. Yet, the proposed recursion displays a number of advantages such as facilitating the introduction of observation-driven birth schemes and the modelling the absence of information on the initial number of targets in the scene. The performance of the proposed approach is demonstrated on simulated data.

Item Type: Journal Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Tracking radar -- Mathematics, Computer algorithms, Possibility -- Data processing, Multisensor data fusion, Random sets
Journal or Publication Title: IEEE Transactions on Signal Processing
Publisher: IEEE Computer Society
ISSN: 1053-587X
Official Date: 4 May 2021
Dates:
DateEvent
4 May 2021Published
28 April 2021Accepted
Volume: 69
Page Range: pp. 2740-2751
DOI: 10.1109/TSP.2021.3077304
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2021 IEEE. 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: 24 June 2021
Date of first compliant Open Access: 24 June 2021

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