刘海涛^{1, 2},
林艳明¹,
陈永华¹,
周尔民^1,,,
彭博²
1.华东交通大学机电与车辆工程学院 南昌 330013
2.清华大学苏州汽车研究院 苏州 215131
基金项目:国家自然科学基金(51765017)，江西省自然科学基金(20202BABL204043)，江西省重点研发计划(20202BBEL53007)

详细信息

作者简介:刘海涛：男，1986年生，副教授，博士，研究方向为振动噪声控制技术、气动噪声分析、噪声源识别、现代检测技术及其应用
林艳明：男，1995年生，硕士生，研究方向为声源追踪
陈永华：男，1994年生，硕士生，研究方向为声信号处理
周尔民：男，1962年生，教授，主要研究方向为智能检测技术及先进制造技术
彭博：男，1989年生，高级工程师，主要研究方向为智能信号处理技术

通讯作者:周尔民　zhouermin@sina.com

中图分类号:TN713; TN911.7

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出版历程

收稿日期:2020-07-08
修回日期:2020-12-09
网络出版日期:2020-12-31
刊出日期:2021-12-21

A Study on Resampling Strategy of Intelligent Particle Filter Based on Genetic Algorithm

Haitao LIU^{1, 2},
Yanming LIN¹,
Yonghua CHEN¹,
Ermin ZHOU^1,,,
Bo PENG²
1. School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2. Suzhou Automotive Research Institue, Tsinghua University, Suzhou 215131, China
Funds:The National Natural Science Foundation of China (51765017), The Natural Science Foundation of Jiangxi Province (20202BABL204043), The Key Research and Development Projects of Jiangxi Province (20202BBEL53007)


摘要
摘要:智能粒子滤波通过借鉴遗传算法思想能够减轻粒子退化现象。在基于遗传算法的智能粒子滤波基础上，该文提出对低权值粒子的改进的智能粒子滤波(IIPF)处理策略。在对粒子进行分离、交叉后，优化遗传算子，对低权值粒子进行自适应处理。低权值粒子根据权值大小自行判断是否为底层粒子；底层粒子将直接进行变异，其余低权值粒子将根据变异概率随机变异。仿真结果表明，改进的智能粒子滤波(IIPF)性能优于智能粒子滤波、一般粒子滤波算法和拓展卡尔曼滤波。在1维仿真实验中，改进的智能粒子滤波误差较一般粒子滤波算法和智能粒子滤波分别降低了10.5%和8.5%，且具有更好的收敛性；在多维仿真实验中，改进的智能粒子滤波较智能粒子滤波在高度均方根误差和平均误差上分别降低了8.5%和7.5%，在速度均方根误差和平均误差上分别降低了11.5%和7.6%；在乘性噪声和非高斯随机噪声中，改进的智能粒子滤波依旧有10%以上的性能优势。
关键词:粒子滤波/
遗传算法/
粒子退化/
自适应
Abstract:The intelligent Particle Filter (PF) based on the genetic algorithm can reduce particle degradation. An adaptive processing strategy for low weight particles is proposed for an Intelligent Particle Filter (IPF) based on the genetic algorithm. After the particles are separated and crossed, the genetic operators are optimized to deal with the low weight particles adaptively. Low weight particles determine whether they are the bottom particle according to the weight size. Then the bottom particles mutate directly, and the rest low-weight particles mutate randomly according to the mutation probability. Simulation results show that the performance of the Improved Intelligent Particle Filter (IIPF) is better than intelligent particle filter, general particle filter algorithms and extended Kalman filter. In the one-dimensional simulation experiment, the error of the improved intelligent particle filter is reduced by 10.5% and 8.5% compared with general particle filters and intelligent particle filter, and the improved intelligent particle filter has better convergence. In the multi-dimensional simulation experiment, the improved intelligent particle filter reduces the root-mean-square error and average error of the altitude by 8.5% and 7.5%, and the root-mean-square error and average error of the speed by 11.5% and 7.6%, respectively. Moreover, under the cases of multiplicative noise and non-Gaussian random noise, the improved intelligent particle filter still has more than 10% performance advantage.
Key words:Particle Filtering (PF)/
Genetic algorithm/
Particle degradation/
Adaptive



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