论文标题
两阶段的贝叶斯优化，用于自动调整无气味的卡尔曼滤波器，用于车辆侧滑角度估计
A Two-Stage Bayesian Optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation
论文作者
论文摘要
本文提出了一种新的方法，可以自动调整无气味的卡尔曼过滤器（UKF）。它涉及基于T-Student过程，使用两阶段的贝叶斯优化（TSBO）来优化UKF的过程噪声参数以进行车辆侧滑角度估计。我们的方法可最大程度地减少性能指标，这是由涵盖各种车辆行为的各种车辆操纵的状态和测量估计误差的平均总和给出的。预定义的成本函数通过TSBO最小化，旨在找到可行区域中的位置，以最大化改善当前最佳解决方案的可能性。实验数据集中的结果表明，与使用遗传算法（GA）相比，将UKF调整为79.9％的能力以及在实验测试数据集中提高估计性能的总能力，以提高最新目前的实验测试数据集。
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.
