自适应无迹增量滤波方法

傅惠民; 吴云章; 娄泰山

自适应无迹增量滤波方法

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: 2013-02-28

Adaptive unscented incremental filter method

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

摘要

摘要: 提出自适应无迹增量滤波(AUIF)的概念和定义,建立自适应无迹增量滤波模型及其分析方法,给出递推算法.传统的滤波方法极少关注量测方程的系统误差.在许多实际情况(如深空探测),量测方程由于受环境因素及测量设备不稳定等影响往往无法进行验证或校准而存在未知的系统误差,并且模型参数和噪声统计量也具有不确定性.这种不确定性会使递推过程产生较大误差,甚至导致发散,从而降低滤波精度.提出的AUIF能够成功消除这种未知的系统误差,也能够实时估计变化的噪声统计量,提高滤波精度.该方法计算简单,便于工程应用.
- 自适应无迹增量滤波 /
- 自适应无迹滤波 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: The adaptive unscented incremental filter(AUIF)model was put forward, in which its concept, model,basis equations and the recursive calculative steps were established. Classical filters did not research the system errors of measurement equations. Due to environmental factors and the instability of measurement equipments, it is difficult to accurately obtain the measurement equations that are not verified and calibrated. So the measurement data have unknown time-varying system errors in actual engineering in the actual environment (such as deep space exploration). The model equations and the noise characteristics have many uncertainties. The uncertainty will lead to greater Kalman filtering error and indeed diverges. The presented AUIF can successfully eliminate the measurement equation system errors. The method can estimate statistical characteristics, adjust gain matrix in real time and greatly improve the filtering accuracy. The method is simple to calculate and easy to apply in engineering.
- adaptive unscented incremental filter(AUIF) /
- adaptive unscented filter(AUF) /
- system error /
- filtering accuracy /
- deep space exploration

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参考文献(15)

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