車両横滑り角推定のためのUKF自動チューニング：二段階ベイズ最適化
A Two-Stage Bayesian Optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation
車両横滑り角推定に用いる無香カルマンフィルタのプロセスノイズパラメータを、t分布過程に基づく二段階ベイズ最適化で自動調整し、遺伝的アルゴリズムより高速かつ高精度に推定できることを示した。
著者: A. Bertipaglia, B. Shyrokau, M. Alirezaei, R. Happee
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.