自校准Kalman滤波方法

傅惠民; 吴云章; 娄泰山; 肖强

doi:10.13224/j.cnki.jasp.2014.06.015

自校准Kalman滤波方法

doi: 10.13224/j.cnki.jasp.2014.06.015

北京航空航天大学小样本技术研究中心, 北京 100191

基金项目:

国家重点基础研究发展计划（2012CB720000）

详细信息

作者简介:
傅惠民（1956- ），男，浙江遂昌人，“长江学者”特聘教授，博士，从事小样本信息技术、软校准技术、数据融合方法、可靠性及滤波理论研究.

中图分类号: V448;O231
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出版历程
- 收稿日期: 2014-02-28
- 刊出日期: 2014-06-28

Self-calibration Kalman filter method

Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 提出一种自校准Kalman滤波方法（SKF），建立SKF模型及其滤波递推算法.在深空探测、发动机故障诊断等许多工程实际中，由于未知输入（如突风、故障、未知的系统误差等）的影响，传统的Kalman滤波方法在滤波递推过程中会产生较大误差.文中提出的自校准Kalman滤波方法能够自动补偿这种未知输入的影响，提高滤波精度.从某飞行器仿真中可以看到，SKF的滤波误差均值和方差分别比传统的Kalman滤波方法降低了400%和300%以上，有效地改善了滤波效果.并且该方法计算简单，便于工程应用.
- 自校准Kalman滤波 /
- 未知输入 /
- 滤波精度 /
- 故障诊断 /
- 深空探测
Abstract: A self-calibration Kalman filter(SKF)method, whose model and recursive algorithm were established, was presented. In most practical cases, such as deep space exploration and engine fault diagnosis, because of the effect of unknown inputs, such as gust, fault and unknown system error, the well-known Kalman filter will lead to greater filtering error in recursive process. To solve this problem, the proposed SKF, which is applied to estimate and compensate the unknown inputs, efficiently reduces the effect of the unknown inputs and enhances filtering accuracy. For some spacecraft navigation simulation, the mean and variance of estimated state errors by SKF decreased by at least 400% and 300%, respectively. The SKF method can be effective to improve the performance of filter, simple to calculate and easy to apply in engineering.
- self-calibration Kalman filter(SKF) /
- unknown input /
- filtering accuracy /
- fault diagnosis /
- deep space exploration

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参考文献(20)

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