摘要
粒子滤波算法在非线性滤波领域受到广泛关注,但是该算法存在样本退化问题.为了改进粒子滤波算法的性能,这里结合自适应优化机制对粒子滤波算法的建议分布选择机制及重采样技术进行改进.对于粒子滤波的建议分布选择,提出一种基于自适应退火参数优化的混合建议分布方法.通过混合建议分布不足的分析,利用退火参数来优化控制状态转移先验分布函数和观测似然函数之间的比例,同时,基于自适应参数优化机制来动态调整退火参数的值.对于粒子滤波的重采样,提出了基于部分分层重采样优化算法的自适应重采样技术.通过有效样本大小的评估来执行自适应重采样策略,此外,基于部分分层重采样算法,利用权重优化的思想对其重采样前后权重计算的方法进行优化.通过相关算法的性能比较,所提改进粒子滤波算法的有效性得以验证.
Although it has attracted widespread attentions in the nonlinear filtering field, particle filter algorithm exists the sample deg- radation problem. In order to improve the algorithm performance, proposal distribution choice and resampling technique of particle fil- ter algorithm are improved by the adaptive optimization mechanism. For proposal distribution choice, it proposed a method of hybrid proposal distribution based on adaptive annealing parameter optimization. With the deficient analysis of hybrid proposal distribution, annealing parameter factor is utilized to adjust the mix ratio of state transition prior distribution and likelihood proposal distribution in hybrid proposal distribution. At the same time, adaptive parameter optimization mechanism is integrated into above hybrid proposal distribution to dynamically adjust the annealing parameter. For resampling technique, it presented an adaptive resampling based on an optimized partial stratified resampling （PSR） algorithm. Effect sample size is estimated so as to implement adaptive resampling. In addition, combined with PSR algorithm, weight computation before and after resampling algorithm is optimized based on weight opti- mization idea. By the comparison with other algorithms, the effectiveness of improved particle filter algorithm introduced in this paper is demonstrated.
出处
《小型微型计算机系统》
CSCD
北大核心
2013年第6期1446-1450,共5页
Journal of Chinese Computer Systems
基金
河南省高校科技创新人才支持计划项目(2009HASTIT021)资助
河南省高等学校青年骨干教师计划项目(2010GGJS-059)资助
河南理工大学青年骨干教师基金项目资助
河南理工大学博士基金项目(2011-58)资助
关键词
粒子滤波
自适应优化机制
混合建议分布
自适应退火参数优化
部分分层重采样
权重优化
particle filter
adaptive optimization mechanism
hybrid proposal distribution
adaptive annealing parameter optimization
partial stratified resampling
weight optimization
