| 用于状态估计的自适应粒子滤波 |
| |
| 引用本文： | 邓小龙,谢剑英,郭为忠.用于状态估计的自适应粒子滤波[J].华南理工大学学报(自然科学版),2006,34(1):57-61. |
| |
| 作者姓名： | 邓小龙 谢剑英 郭为忠 |
| |
| 作者单位： | 1. 上海交通大学,自动化系,上海,200030
2. 上海交通大学,机械与动力工程学院,上海,200030 |
| |
| 摘 要： | 分析了粒子滤波的性能关键，提出了一种新的自适应粒子滤波算法．该算法采用一种新提议分布，即将UKF（Unscented Kalman Filter）与自适应强跟踪滤波器（STF）相结合．新提议分布通过UKF构造粒子群，而粒子群中的每个粒子中的每个sigma点用STF来更新，它可以在线调节因子而使得算法自适应．非线性状态估计仿真试验证实了改进的粒子滤波算法的有效性．
|
| 关 键 词： | 粒子滤波 状态估计 自适应滤波 强跟踪滤波器 |
| 文章编号： | 1000-565X（2006）01-0057-05 |
| 收稿时间： | 2005-01-05 |
| 修稿时间： | 2005年1月5日 |
Adaptive Particle Filtration for State Estimation |
| |
| Deng Xiao-long,Xie Jian-ying,Guo Wei-zhong.Adaptive Particle Filtration for State Estimation[J].Journal of South China University of Technology(Natural Science Edition),2006,34(1):57-61. |
| |
| Authors: | Deng Xiao-long Xie Jian-ying Guo Wei-zhong |
| |
| Affiliation: | 1. Dept. of Automation, Shanghai Jiaotong Univ. , Shanghai 200030, China; 2. School of Mechanical Engineering, Shanghai Jiaotong Univ. , Shanghai 200030, China |
| |
| Abstract: | This paper analyzes the keys for the performance of particle filter(PF) and presents a new adaptive PF algorithm.The algorithm adopts a new proposal distribution combining the unscented Kalman filter(UKF) with the adaptive strong tracking filter(STF).The new proposal distribution adopts UKF to produce the particles,in which each sigma point of every particle is updated by STF.Moreover,the added scaling factor can be adjusted on line to make the algorithm adaptive.Simulated experiments of nonlinear state estimation are finally carried out to confirm the validity of the improved PF algorithm. |
| |
| Keywords: | UKF |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
