基于自适应粒子群优化的粒子滤波跟踪算法  

Particle filtering tracking algorithm based on adaptive particle swarm optimization

作　　者：林晓杰 索继东[1] LIN Xiaojie;SUO Jidong(College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学信息科学技术学院,辽宁大连116026

出　　处：《现代电子技术》2020年第17期11-15,共5页Modern Electronics Technique

基　　金：国家科技支撑计划子课题(2015BAG20B02);辽宁省博士启动基金(201601065);福建海事局项目(2018Z0093)。

摘　　要：传统粒子滤波算法中在重要性采样部分存在采样粒子位置不精确的问题,可用粒子群优化算法优化,但目前的标准粒子群优化粒子滤波算法会出现粒子局部寻优的情况。对此对算法中的惯性权重和学习因子同时采取自适应调整的方法,平衡粒子的搜索能力以减少这种情况的出现,并且为了解决算法优化后因粒子聚集而造成的多样性缺失问题,对粒子进行随机变异以提高粒子多样性。仿真结果表明,经过改进后的优化算法可有效提高粒子滤波算法的准确性,使跟踪误差减小。The problem of inaccurate sampling particle position exists in the important sampling part of the traditional particle filter algorithm,which can be optimized by particle swarm optimization algorithm. However,the current standard particle swarm optimization particle filtering algorithm may lead to particle local optimization. In this paper,self-adaptive adjustment method is adopted to adjust the inertia weight and learning factor in the algorithm,so as to balance the searching ability of particles to reduce the occurrence of this situation. In order to solve the problem of diversity lack caused by particle aggregation after optimization of the algorithm,the particles are randomly mutated to improve the diversity of particles. The simulation results show that the improved optimization algorithm can effectively improve the accuracy of particle filtering algorithm and reduce the tracking error.

关 键 词：粒子滤波跟踪 粒子群优化 自适应调整 搜索能力平衡 随机变异 优化算法 

分 类 号：TN911.1-34[电子电信—通信与信息系统] TP391.9[电子电信—信息与通信工程]