自适应增量粒子滤波方法

傅惠民; 吴云章; 娄泰山

自适应增量粒子滤波方法

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: 2012-07-24
- 刊出日期: 2013-08-28

Adaptive incremental particle filter method

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

摘要

摘要: 提出自适应增量粒子滤波(AIPF)的概念和定义,建立AIPF模型,给出了分析方法和主要的计算步骤.对于许多实际工程(如深空探测)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及滤波过程中的粒子匮乏等问题,通过增量粒子滤波模型对滤波过程中的粒子数进行自适应调整,从而消除这种未知系统和滤波粒子匮乏的影响,自动调整粒子,提高非线性滤波的精度.仿真计算中,滤波误差均值和方差分别降低为原来的3.8%和19.6%.该方法有效地改善了滤波效果,计算简单,便于工程应用.
- 自适应增量粒子滤波 /
- 自适应粒子滤波 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: An adaptive incremental particle filter (AIPF) model was put forward, and its concept, model, basic equations and key calculative steps were given. For the measurement data with unknown system errors in many actual engineering (such as deep space exploration) and the considerable filter errors, accurate measurement model cannot be established. The presented AIPF method applied the accurate incremental particle filter model to automatically conduct adaptive adjustment of the number of particles, so the effects of these unknown measurement system errors and the lack of particles were eliminated. This method can automatically adjust the particles (sample points) and finally improve the nonlinear filtering accuracy. In simulation, the mean and covariance of filtering error decrease by 3.8% and 19.6%, respectively. The method can effectively improve the performance of filter, so it can be easily applied to engineering with simple calculation process.
- adaptive incremental particle filter(AIPF) /
- adaptive particle filter(APF) /
- system error /
- filtering accuracy /
- deep space exploration

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参考文献(15)

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