Performance evaluation of simultaneous sensor registration and object tracking algorithm

Macdonald, Sofie and Proudler, Ian and Davies, Michael E. and Hopgood, James R.; (2022) Performance evaluation of simultaneous sensor registration and object tracking algorithm. In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2022. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems . IEEE, Piscataway, NJ. ISBN 9781665460262 (https://doi.org/10.1109/mfi55806.2022.9913857)

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Abstract

Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited – such as underwater, or in hostile regions – this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m.

  • Item type:Book Section
    ID code:82985
    Dates:
    Date
    Event
    13 October 2022
    Published
    19 August 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:31 Oct 2022 11:00
    Last modified:08 Apr 2024 14:19
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/82985
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