多模型自校准无迹Kalman滤波方法

杨海峰; 王宇翔

doi:10.13224/j.cnki.jasp.20220516

多模型自校准无迹Kalman滤波方法

doi: 10.13224/j.cnki.jasp.20220516

杨海峰^1,,
王宇翔²

1.
工业和信息化部高新技术司，北京 100804
2.
华中科技大学机械科学与工程学院，武汉 430074

基金项目: 国家自然科学基金面上项目（61972021）

详细信息

作者简介:
杨海峰（1993-），男，博士，主要从事滤波算法、自主导航、深空探测等方面的研究。E-mail：halfyang@buaa.edu.cn

中图分类号: V448；O231
计量
- 文章访问数: 12
- HTML浏览量: 5
- PDF量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2022-07-19
- 网络出版日期: 2024-02-28

Multiple-model self-calibration unscented Kalman filter method

YANG Haifeng^1
,,
WANG Yuxiang²

1.
High-tech Department，Ministry of Industry and Information Technology，Beijing 100804，China
2.
School of Mechanical Science and Engineering，Huazhong University of Science and Technology，Wuhan 430074，China

摘要

摘要:
基于无迹Kalman滤波方法（UKF）、自校准无迹Kalman滤波方法（SUKF）和多模型估计理论（MME），针对工程实际中强非线性系统状态方程受未知输入（如医用机械臂惯导单元的零漂误差、列车行驶中遭遇突风和机载元器件故障等）影响的问题，提出了一种多模型自校准无迹Kalman滤波方法（MSUKF），将多模型自校准Kalman滤波方法（MSKF）的适用范围扩展到了强非线性领域。该方法同时采用UKF与SUKF进行计算，根据贝叶斯定理实时分配两者先验估计值的权重，通过加权融合进而得到最终的状态估计。大量数值仿真结果表明：本文方法精度比滤波发散的UKF提高了50%，与无偏的SUKF相比也提升了4%以上，具有更强的适应性和鲁棒性。
- 自校准滤波 /
- 多模型估计 /
- 无迹Kalman滤波 /
- 未知输入 /
- 故障诊断
Abstract:
Based on the unscented Kalman filter (UKF), the self-calibration unscented Kalman filter (SUKF) and the multiple-model estimation (MME), considering the influences of unknown inputs (such as drift error of the IMU in medical manipulator, gust encountered by the running train and failure of onboard components) on the strongly nonlinear system state equation in engineering, the multiple-model self-calibration unscented Kalman filter (MSUKF) was proposed to expand the application scope of the multiple-model self-calibration Kalman filter (MSKF). According to the Bayes' theorem, this filtering method used the UKF and the SUKF whose weights were assigned automatically to obtain the final filtering result through weight-average way. A large number of simulation results showed that the accuracy of MSUKF was 50% higher than that of divergent UKF, and 4% higher than that of unbiased SUKF, presenting stronger adaptability and robustness.
- self-calibration filter /
- multiple-model estimation /
- unscented Kalman filter /
- unknown input /
- fault diagnosis

HTML

图 1 模型概率比较

Figure 1. Comparison of model probability

下载: 全尺寸图片幻灯片

图 2 状态误差比较

Figure 2. Comparison of state error

下载: 全尺寸图片幻灯片

图 3 UKF、SUKF和MSUKF方法的方均根误差比较

Figure 3. Comparison of root mean square error of UKF，SUKF and MSUKF methods

下载: 全尺寸图片幻灯片

图 4 UKF和SUKF方法概率均值比较

Figure 4. Comparison of probability mean of UKF and SUKF methods

下载: 全尺寸图片幻灯片

表 1 UKF、SEKF和MSEKF方法的方均根误差均值

Table 1. Mean of root mean square error of UKF，SUKF and MSUKF methods

方法方均根误差均值

MSUKF 0.086
SUKF 0.090
UKF 0.173

下载: 导出CSV

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