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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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WRAP-linear-algorithm-multi-target-tracking-possibility-theory-2021.pdf - Accepted Version - Requires a PDF viewer. Download (985Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TSP.2021.3077304
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: |
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| 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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