Implementation of adaptive kernel Kalman filter in stone soup

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Wright, James S. and Hopgood, James R. and Davies, Mike E. and Proudler, Ian K. and Sun, Mengwei; (2023) Implementation of adaptive kernel Kalman filter in stone soup. In: 2023 Sensor Signal Processing for Defence Conference (SSPD). IEEE, Piscataway, NJ, pp. 1-5. ISBN 9798350337327 (https://doi.org/10.1109/sspd57945.2023.10256739)

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Abstract

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing–only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

  • Item type:Book Section
    ID code:86893
    Dates:
    Date
    Event
    22 September 2023
    Published
    23 June 2023
    Accepted
    Notes:© 2023 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:09 Oct 2023 08:22
    Last modified:11 Nov 2024 15:34
    URI:https://strathprints.strath.ac.uk/id/eprint/86893
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