Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method

IF 0.7 Q4 ENGINEERING, MECHANICAL
Yingjie Liu, Dawei Cui, Wen Peng
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引用次数: 0

Abstract

A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.
基于自适应反离群无特征卡尔曼滤波器和 GA-BPNN 方法的车辆状态和参数估计
针对车辆状态估计中遇到的异常数据处理问题,提出了一种多机学习改进型自适应卡尔曼滤波方法。首先,通过引入经遗传算法改进的 BP 神经网络(GA-BPNN)对无特征卡尔曼滤波(UKF)算法进行改进,以调节和修正 UKF 方法的全局误差。然后,应用反离群技术全面消除测量中的孤立离群和斑点离群,实现了对 GA-BPNN-UKF 的进一步改进,显著提高了滤波过程的鲁棒性。最后,通过仿真验证了所提出的新算法的有效性,并对其结果进行了分析,为进一步的实际应用提供了可靠的依据。仿真结果表明,GA-BPNN 算法的估计性能明显优于扩展卡尔曼滤波(EKF)方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
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