A Gaussian process based method for multiple model tracking

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Sun, Mengwei and Davies, Mike E. and Proudler, Ian and Hopgood, James R.; (2020) A Gaussian process based method for multiple model tracking. In: 2020 Sensor Signal Processing for Defence Conference, SSPD 2020. 2020 Sensor Signal Processing for Defence Conference, SSPD 2020 . IEEE, GBR. ISBN 9781728138107 (https://doi.org/10.1109/SSPD47486.2020.9272174)

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Abstract

Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e., unknown model types or uncertain model parameters. Multiple-model (MM) methods have been generally considered to deal with this challenge, in which a bank of elemental filters is run simultaneously to provide a joint decision and estimation of motion model and localisation. However, if the uncertainty of the target trajectory increases, such as the target moves under mixed manoeuvring behaviours with time-varying parameters, more filters with different model assumptions have to be taken into account to match the motion of the target, which may lead to prohibitive computational complexity. To address this problem, we establish a training based algorithm which can learn the actual motion model as a Gaussian process (GP) regression. Then, by integrating the trained GP into the particle filter (PF), a GP-PF based tracking method is developed to track the manoeuvring targets in real-Time. Monte Carlo experiments show that the proposed method had much lower tracking root mean square error (RMSE) and robustness compared with the most commonly used MM methods.

  • Item type:Book Section
    ID code:77268
    Dates:
    Date
    Event
    15 September 2020
    Published
    17 April 2020
    Accepted
    Notes:© 2020 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:03 Aug 2021 10:35
    Last modified:16 Dec 2024 01:13
    URI:https://strathprints.strath.ac.uk/id/eprint/77268
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