摘要
在当前的粒子滤波中,粒子可能出现退化现象和重采样,导致样本枯竭从而破坏粒子多样性。针对该问题,借鉴知识板和协同进化理论,提出一种基于知识板的协同粒子滤波算法。该算法对重要性密度函数进行采样,形成采样粒子样本,并将粒子划分为若干个子采样粒子群,对每个子采样粒子群在不同的区域进行搜索,通过子采样粒子群之间的通信,最终找到动态系统的最佳状态估计。理论分析与仿真结果表明,该算法能提高经典粒子滤波算法的群体多样性,在加快收敛速度和降低计算复杂度方面有较大优势。
For the degeneracy phenomenon of particles caused by evoluting and the impoverishment problem of particles caused by resampling. This paper proposes a novel particle filtering algorithm based on knowledge plate and coevolution. The main idea of this algorithm is to sample from the importance density function and generate particle samples which are divided into several sub-sample groups. Each sub-sample group searches among different area and finds the optimal state estimation of this dynamical system by means of the communication between each other. Theoretical analysis and experimental simulation results show that the proposed algorithm improves population diversity and has potential advantages in convergence rate and computational complexity, thus enhances the searching performance of the algorithm.
出处
《计算机工程》
CAS
CSCD
2014年第3期228-231,237,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61073091
61100173)
陕西省自然科学基金资助项目(2010JM8028)
关键词
粒子滤波
多样性
知识板
协同
采样
优化
particle filtering
diversity
knowledge plate
cooperative
sampling
optimization
