摘要
为解决粒子滤波算法中存在的粒子退化和样本枯竭问题,提出一种新的粒子滤波算法。利用粒子群优化思想促使采样粒子向高似然区域移动,减缓粒子权值的退化;再通过人工免疫算法中的变异操作扩大算法寻找最优值的范围并增加粒子的多样性,避免算法陷入局部最优,增强算法的全局搜索能力,进而缓解样本枯竭。实验表明,该算法比标准粒子滤波的状态估计精度提高近40倍,比扩展卡尔曼粒子滤波提高近28倍,比无迹卡尔曼粒子滤波提高近6倍,滤波效率为37.523%,是标准粒子滤波的37倍,该算法具有更好的实时性和更高的状态估计精度,能有效缓解粒子的退化和样本的枯竭。
To solve the particle weights degradation and sample impoverishment problems in particle filter which was used in non-Gauss- Jan nordktear systems, a new kind of particle filter was proposed. The sampling particles could move to high likelihood area by using particle swarm optimization idea, and the weights of degradation of the particles were reduced. Then through the variation operation of the artificial immune, the range of the optimal value was expanded when searching for the optimization, and the diversity of the particles was increased, Simulation results showed that the state estimation accuracy of new algorithm is improved nearly 40 times than standard particle filter. The filtering efficiency is 37. 523%, 37 times of the standard particle filter. This algorithm has better real-time and state estimation precision, and could effectively relieve the particle of the weights Of the exhaustion of sample degradation.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2013年第1期146-151,共6页
Journal of Sichuan University (Engineering Science Edition)
基金
四川省青年科技基金资助项目(2010JQ0041)
四川省应用基础研究项目(2011JY0115)
关键词
粒子群优化
人工免疫
粒子滤波
particle swarm optimization
artificial immune
particle filter
