Adaptive kernel Kalman filter based belief propagation algorithm for maneuvering multi-target tracking

Sun, Mengwei and Davies, Mike E. and Proudler, Ian K. and Hopgood, James R. (2022) Adaptive kernel Kalman filter based belief propagation algorithm for maneuvering multi-target tracking. IEEE Signal Processing Letters, 29. pp. 1452-1456. ISSN 1070-9908 (https://doi.org/10.1109/lsp.2022.3184534)

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Abstract

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based multi-target tracking (MTT) BP algorithm.

  • Item type:Article
    ID code:81354
    Dates:
    Date
    Event
    20 June 2022
    Published
    20 June 2022
    Published Online
    20 June 2022
    Accepted
    Notes:© 2022 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.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:06 Jul 2022 10:06
    Last modified:11 Nov 2024 13:33
    URI:https://strathprints.strath.ac.uk/id/eprint/81354
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