摘要
针对非线性、非高斯系统状态的在线估计问题,提出了一种改进的粒子滤波算法。该算法采用Unscented卡尔曼滤波器(UKF)产生系统的状态估计,并在量测更新过程中加入衰减记忆因子,消弱滤波器对历史信息的依赖,增强当前量测信息对滤波器的修正作用,从而产生一个优选的建议分布函数,较好地抑制了粒子退化问题。理论分析和实验表明:引入记忆衰减因子的粒子滤波,即衰减记忆无味粒子滤波(MAUPF)的性能明显优于标准的粒子滤波以及Unscented粒子滤波。
A new particle filter is proposed for the on-line estimation problem of non-Gauss nonlinear systems. In order to weaken the effect of historical information and enhance the effect of up-to-date measurement, it introduces attenuation memory factor for generat- ing the important density function based on the Unscented Kalman filter（UKF） for a better performance in inhibiting the particle degradation problems in the new algorithm. As a result, the theoretical analysis and experimental results show that the new particle filter outperforms obviously superior to the standard particle filter and Unscented particle filter.
出处
《计算机工程与应用》
CSCD
2012年第8期144-147,共4页
Computer Engineering and Applications
基金
甘肃省自然科技基金(No.1010RJZA046)
甘肃省教育厅硕导基金项目(No.0914ZTB003)
甘肃省财政厅项目(No.0914ZTB148)
关键词
状态估计
粒子滤波器
记忆衰减因子
重要性概率密度函数
state estimation
particle filter
attenuation memory factor
important density function,~ ~ ,~ .~, ]
