增量粒子滤波方法

傅惠民; 娄泰山; 吴云章

增量粒子滤波方法

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: 2012-02-27
- 刊出日期: 2013-06-28

Incremental particle filter method

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

摘要

摘要: 提出增量粒子滤波的概念,建立增量粒子滤波模型及其分析方法,给出其算法.对于工程实际中存在的由未知系统误差的影响而无法精确建立量测似然函数的这一问题,提出增量粒子滤波方法,通过对带有未知系统误差的量测数据进行校正,获得精确的量测似然函数,建立精确的增量粒子滤波模型,从而消除这种未知系统误差的影响,减少重采样的次数,较好地保存了粒子的多样性,提高非线性滤波的精度.模拟仿真中,重采样的次数减少41.7%,滤波误差均值和均方根误差分别降低了45.3%和70.1%,有效地改善了滤波的效果.
- 增量量测方程 /
- 增量粒子滤波 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: An incremental particle filter(IPF)model and analysis method were put forward, while the concept and recursive calculative steps were established. For the measurement data with unknown system errors in practical engineering, accurate measurement model cannot be established. The presented IPF method established accurate measurement likelihood function by calibrating the unknown system errors in the measurement data, and accurate incremental particle filter model was obtained. This can eliminate these unknown system errors, decrease the number of resampling, effectively preserve the diversity of particles and improve the accuracy of nonlinear filtering. In simulation, the number of resampling decreased by 41.7 percentages, and filtering error mean value and RMSE (root mean square error) decreased by 45.3 percentages and 70.1 percentages, respectively, improving the performance of filtering effectively.
- incremental measurement equation /
- incremental particle filter(IPF) /
- system error /
- filtering accuracy /
- deep space exploration

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参考文献(15)

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