自适应扩展增量 Kalman 滤波方法

傅惠民; 吴云章; 娄泰山

自适应扩展增量 Kalman 滤波方法

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)

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出版历程
- 收稿日期: 2012-02-29
- 刊出日期: 2012-08-28

Adaptive extended incremental Kalman filter method

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

摘要

摘要: 基于扩展增量Kalman滤波方法(EIKF)和自适应增量Kalman滤波(AIKF),建立自适应扩展增量Kalman(AEIKF)模型及其分析方法,给出递推算法.在许多实际情况(如深空探测),由于环境因素的影响、测量设备的不稳定性等原因,量测方程往往存在未知的系统误差,并且模型参数也具有不确定性,结果导致较大的Kalman滤波误差,影响滤波的收敛性.提出的AEIKF方法能够成功消除这种未知的系统误差,并能够实时估计变化的噪声统计量,提高Kalman滤波精度.该方法计算简单,便于工程应用.
- 自适应扩展增量Kalman滤波 /
- 扩展Kalman滤波 /
- 自适应扩展Kalman滤波 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: Based on extended incremental Kalman filter (EIKF) and adaptive incremental Kalman filter (AIKF), the adaptive extended incremental Kalman filter (AEIKF) and analysis method were put forward, the key calculative steps were established. The measurement equations have many uncertainties and can't obtain precision parameters in actual environment(such as deep space exploration). Due to environmental factors and the instability of measurement equipments, the measurement data have unknown time-varying system errors in actual engineering, which leads to greater Kalman filtering error. Ultimately, the convergence of Kalman filter were reduced. The presented adaptive extended incremental Kalman filter method can successfully eliminate these unknown system errors. The method can estimate statistical characteristics of noise in real time and greatly improve the accuracy of filter. The method is simple to calculate and easy to apply in engineering.
- adaptive extended incremental Kalman filter(AEIKF) /
- extended Kalman filter(EKF) /
- adaptive extended Kalman filter(AEKF) /
- system error /
- filtering accuracy /
- deep space exploration

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参考文献(15)

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