Performance evaluation of simultaneous sensor registration and object tracking algorithm

Sofie Macdonald, Ian Proudler, Michael E. Davies, James R. Hopgood

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2022
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)9781665460262
DOIs
Publication statusPublished - 13 Oct 2022

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume2022-September

Keywords

  • object tracking
  • Global Navigation Satellite System (GNSS)
  • extended Kalman filter

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