欠观测条件下的扩展增量 <em>Kalman</em> 滤波方法

傅惠民; 娄泰山; 吴云章

欠观测条件下的扩展增量 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: 2012-01-12
- 修回日期: 2012-03-01
- 刊出日期: 2012-04-28

Extended incremental Kalman filter method under poor observation condition

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

摘要

摘要: 建立欠观测条件下的非线性增量量测方程,并给出其线性化方法,在此基础上提出一种欠观测条件下的扩展增量Kalman滤波(EIKF)模型及其递推算法.工程实际中,由于环境因素的影响、测量设备的不稳定性等原因往往带来未知的系统误差,传统的扩展Kalman滤波(EKF)无法对这种未知的系统误差进行补偿和校正,结果产生较大的滤波误差,甚至导致发散.提出的扩展增量Kalman滤波方法能够成功地消除测量的系统误差,从而有效地提高非线性滤波的精度.该方法计算简单,便于工程应用.
- 增量量测方程 /
- 扩展增量Kalman滤波 /
- 欠观测条件 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: A nonlinear incremental measurement equation under poor observation condition was established, and its linearization method was given. Based on this equation, an extended incremental Kalman filter(EIKF)model under poor observation condition and its recursive calculative steps were proposed. Due to environmental factors and the instability of measurement equipments, the measurement data had unknown system errors in engineering. Classical extended Kalman filter(EKF)method cannot compensate and correct the unknown system errors, which produced considerable filter errors and even led to diverge. The presented EIKF method can successfully eliminate these unknown system errors and effectively improve the accuracy of nonlinear filtering. The method is simple to calculate and easy to apply in engineering.
- incremental measurement equation /
- extended incremental Kalman filter(EIKF) /
- poor observation condition /
- system error /
- filtering accuracy /
- deep space exploration

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参考文献(18)

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