非高斯噪声中的粒子滤波算法研究  

Research on Particle Filter Algorithms in the Non-Gassian Noise

作　　者：王晓薇[1] 山拜.达拉拜 陈娟[1] 李婷婷[1] 

机构地区：[1]新疆大学信息科学与工程学院,新疆乌鲁木齐830046

出　　处：《计算机工程与科学》2012年第7期136-139,共4页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(60971130)

摘　　要：在非线性非高斯动态系统中,粒子滤波已成为解决系统参数估计和状态滤波的主流方法。然而,粒子退化是粒子滤波中不可避免的现象,粒子重采样是解决方法之一。本文针对粒子退化现象,在扩展卡尔曼滤波器的基础上研究了一种基于支持向量机粒子滤波算法,算法实现中扩展卡尔曼粒子滤波器结合支持向量机对当前时刻的重要性采样,再对粒子样本进行重采样。该算法能有效地利用量测值的最新信息,状态估计误差较小,同时避免了粒子匮乏。理论分析和仿真结果表明,新算法在双模噪声非线性系统估计的精度优于标准粒子滤波算法与扩展卡尔曼粒子滤波算法。The particle filter has become the mainstream method for solving system parameter estimation and the state of filter in nonlinear non-gaussian dynamic systems.However the particle degradation problem in particle filter is an inevitable phenomenon and the solution is particle resampling.According to the particle degradation phenomenon of the existing defects,there will be a new mixed particle filter proposed in this paper based on the extended Kalman particle filter.In the new algorithm,the extended Kalman particle filter with support vector machine（SVM） implements the present moment sampling and resampling.This structure makes use of the latest observation information avoiding the lack of particles.It has small errors and better stability.Theoretical analysis and simulation results show that the new method outperform the interacting standard particle filter and the extended Kalman particle filter in the filter precision of double-modal noise system state.

关 键 词：粒子滤波 重采样 支持向量机 双模噪声 

分 类 号：TN911.23[电子电信—通信与信息系统]