基于鸡群优化的粒子滤波算法研究

doi:10.16182/j.issn1004731x.joss.201702008

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (2): 295-300.doi: 10.16182/j.issn1004731x.joss.201702008

基于鸡群优化的粒子滤波算法研究

张建春^1,2, 康凤举^1,2, 梁洪涛^1,2, 徐皓^1,2

1.西北工业大学航海学院,西安 710072;
2.水下信息处理与控制国家级重点实验室,西安 710072

收稿日期:2016-06-19 修回日期:2016-08-24 出版日期:2017-02-08 发布日期:2020-06-01
作者简介:张建春(1988-),男,浙江龙游,博士生,研究方向为系统建模与仿真;康凤举(1947-),男,江苏南通,本科,教授,研究方向为系统建模与仿真、先进控制技术。

Research on Chicken Swarm Optimization-based Particle Filter

Zhang Jianchun^1,2, Kang Fengju^1,2, Liang Hongtao^1,2, Xu Hao^1,2

1. Marine College, Northwestern Polytechnical University, Xi'an 710072, China;
2. National Key Laboratory of Underwater Information Process and Control, Xi'an 710072, China

Received:2016-06-19 Revised:2016-08-24 Online:2017-02-08 Published:2020-06-01

摘要/Abstract

摘要： 针对粒子滤波中重采样所引起的粒子贫化问题,在将鸡群优化算法融入到粒子滤波采样阶段的基础上,提出了一种鸡群优化粒子滤波算法。该算法将粒子权值作为适应度以确定粒子的类型及相互关系,通过不同类型粒子的运动机制完成相应的位置更新,并利用动态变化的粒子群体结构来克服陷入局部最优的不足和加快寻优速度,使粒子向后验概率的高似然区域运动,既保证了样本多样性又提高了粒子质量。仿真实验结果表明该方法提高了滤波的估计精度并保持了滤波过程中粒子的多样性,同时减少了状态估计所需的粒子数量。

关键词: 鸡群优化算法, 粒子滤波, 粒子贫化, 状态估计

Abstract: To solve the particle impoverishment caused by resampling in particle fileter (PF), the Chicken Swarm Optimization (CSO) was integrated into the sampling phase of generic particle filter and an intelligent optimized particle filter of CSO was proposed. According to the weight of focused particles as the fitness, the type of each particle in the population and interrelation between each one was determined. Various designed mechanisms about individual movement were introduced to update the location. Moreover, the dynamical structure of particle population was utilized to overcome weakness of local optimum and improve the optimization. On the basis, particles moved towards to the high likelihood region of posterior probability density. As a result, the diversity of samples was kept and quality of particles was ameliorated. The result of simulation experiment shows that this algorithm has higher estimation accuracy and keeps the diversity of particles, and reduces the quantity of particles required by the state estimation.

Key words: chicken swarm optimization, particle filter, particle impoverishment, state estimation

中图分类号:

TP391.9

张建春, 康凤举, 梁洪涛, 徐皓. 基于鸡群优化的粒子滤波算法研究[J]. 系统仿真学报, 2017, 29(2): 295-300.

Zhang Jianchun, Kang Fengju, Liang Hongtao, Xu Hao. Research on Chicken Swarm Optimization-based Particle Filter[J]. Journal of System Simulation, 2017, 29(2): 295-300.

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基于鸡群优化的粒子滤波算法研究

Research on Chicken Swarm Optimization-based Particle Filter

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