A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

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This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states’ and measurement’ estimation error for various vehicle manoeuvres covering a wide range of vehicle behaviour. The predefined cost function is minimised through a TSBO which aims to find a location in the feasible region that maximises the probability of improving the current best solution. Results on an experimental dataset show the capability to tune the UKF in 79.9% less time than using a genetic algorithm (GA) and the overall capacity to improve the estimation performance in an experimental test dataset of 9.9% to the current state-of-the-art GA.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV)
ISBN (Electronic)978-1-6654-8821-1
Publication statusPublished - 2022
Event2022 IEEE Intelligent Vehicles Symposium (IV) - Aachen, Germany
Duration: 5 Jun 20229 Jun 2022


Conference2022 IEEE Intelligent Vehicles Symposium (IV)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Training
  • Intelligent vehicles
  • Measurement uncertainty
  • Gaussian processes
  • Cost function
  • Bayes methods
  • Kalman filters

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