摘要
针对粒子滤波的粒子退化和贫化问题,将新兴的简化群优化(SSO)算法引入到粒子滤波的重采样阶段.SSO算法结构简单,在保留优良粒子的基础上,增加一项粒子随机运动过程,以提供粒子多样性.实验结果表明,新算法不仅有效提高了对非线性系统状态的估计精度,而且具有更高的运算速度.
A new particle filter based on the simplified swarm optimization （called SSO-PF） is proposed for solving the degeneracy and impoverishment problem in the particle filter. The proposed algorithm uses the emerging SSO that is simple as the resampling stage of particle filter. A random movement is added to SSO to maintain particles diversity. Experimental results show that the proposed algorithm not only effectively boosts the estimation accuracy of the nonlinear system state, but also has a higher computing speed.
作者
张义群
林培杰
程树英
ZHANG Yiqun LIN Peijie CHENG Shuying(College of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, Fujian 350116, China)
出处
《福州大学学报（自然科学版）》
CAS
北大核心
2017年第1期102-107,共6页
Journal of Fuzhou University(Natural Science Edition)
基金
国家自然科学基金资助项目(61574038)
福建省科技厅工业引导性重点基金资助项目(2015H0021)
福建省教育厅省属高校基金资助项目(JK2014003)
关键词
粒子滤波
简化群优化
粒子群优化
重采样
粒子退化
particle filter
simplified swarm optimization
particle swarm optimization
resampling
particle degeneracy
