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

Conference paper (2022)

Authors

A. Bertipaglia Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
B. Shyrokau Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Mohsen Alirezaei Eindhoven University of Technology
R. Happee Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 A. Bertipaglia, B. Shyrokau, Mohsen Alirezaei, R. Happee
DOI: https://doi.org/10.1109/IV51971.2022.9826998
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

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.

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