多模型自校准Kalman滤波方法

杨海峰; 王金娜; 王宇翔

doi:10.13224/j.cnki.jasp.20220244

多模型自校准Kalman滤波方法

doi: 10.13224/j.cnki.jasp.20220244

1.
工业和信息化部高新技术司，北京 100804
2.
西安交通大学能源与动力工程学院，西安 710049
3.
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
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- 文章访问数: 124
- HTML浏览量: 47
- PDF量: 39
- 被引次数: 0
出版历程
- 收稿日期: 2022-04-24
- 网络出版日期: 2023-09-14

Multiple-model self-calibration Kalman filter method

1.
High-tech Department， Ministry of Industry and Information Technology，Beijing 100804，China
2.
School of Energy and Power Engineering，Xi’an Jiaotong University，Xi’an 710049，China
3.
School of Mechanical Science and Engineering， Huazhong University of Science and Technology，Wuhan 430074，China

摘要

摘要:
基于自校准Kalman滤波方法和多模型估计理论，针对工程实际中未知输入（如突风、故障和未知系统误差等）对系统状态方程的影响问题，提出了一种多模型自校准Kalman滤波方法。该方法同时采用自校准Kalman滤波和标准Kalman滤波进行运算，并根据贝叶斯定理自动分配两种方法滤波值的权重，通过加权融合得到最终的滤波结果。与自校准Kalman滤波方法相比，多模型自校准Kalman滤波方法既能有效地补偿非零未知输入的影响，又明显改善了系统在未知输入为零时的滤波精度，大量数值仿真结果表明该方法精度提升可达10%以上，具有更强的适应性和鲁棒性。
- 自校准滤波 /
- 多模型估计 /
- Kalman滤波 /
- 未知输入 /
- 故障诊断
Abstract:
Based on the self-calibration Kalman filter (SKF) and the multiple-model estimation (MME), considering the influence of unknown inputs (such as gusts, faults, unknown system errors, etc.) on the system state equation in Engineering, the multiple-model self-calibration Kalman filter (MSKF) was proposed. According to the Bayes' theorem, this filtering method used the SKF and the standard Kalman filter (KF) whose weights were assigned automatically to obtain the final filtering result through weight-average way. Compared with the SKF, the MSKF can not only effectively compensate the effects of non-zero unknown inputs, but also improve the estimation accuracy when unknown inputs were zero. A large number of simulation results showed that accuracy can be improved by more than 10%, using the proposed method. In summary, the MSKF has stronger adaptability and robustness.
- self-calibration filter /
- multiple-model estimation /
- Kalman filter /
- unknown input /
- fault diagnosis

HTML

图 1 状态误差比较

Figure 1. Comparison of state errors

下载: 全尺寸图片幻灯片

图 2 方均根误差比较

Figure 2. Comparison of RMSEs

下载: 全尺寸图片幻灯片

图 3 各维度状态误差比较

Figure 3. Comparison of state errors in two dimensions

下载: 全尺寸图片幻灯片

图 4 各维度方均根误差比较

Figure 4. Comparison of RMSEs in two dimensions

下载: 全尺寸图片幻灯片

表 1 方均根误差均值

Table 1. Mean of RMSEs

方法方均根误差均值

MSKF 0.0372
SKF 0.0418
KF 0.1169

下载: 导出CSV

表 2 各维度方均根误差均值

Table 2. Mean of RMSEs in two dimensions

方法方均根误差均值
维度一维度二

MSKF 0.0335 0.0332
SKF 0.0355 0.0354
KF 0.0915 0.0920

下载: 导出CSV

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