Advanced Search
Volume 42 Issue 5
May  2016
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(5): 885-890.
WANG Ershen, PANG Tao, QU Pingping, et al. Improved particle filter algorithm based on chaos particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(5): 885-890. doi: 10.13700/j.bh.1001-5965.2015.0670(in Chinese)
Citation: WANG Ershen, PANG Tao, QU Pingping, et al. Improved particle filter algorithm based on chaos particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(5): 885-890. doi: 10.13700/j.bh.1001-5965.2015.0670(in Chinese)
shu

Improved particle filter algorithm based on chaos particle swarm optimization

doi: 10.13700/j.bh.1001-5965.2015.0670

  • School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China

  • Received Date: 16 Oct 2015
  • Publish Date: 20 May 2016
    Abstract
  • To solve the degeneracy phenomenon and the sample impoverishment problem of basic particle filter (PF) algorithm, which makes the particles of PF algorithm unable to express the real distribution of probability density function, a novel PF algorithm based on chaos particle swarm was proposed. Chaos sequence was adopted in this proposed algorithm. The chaos sequence was used to generate a set of chaotic variables, which was mapped to the interval of optimization variables to improve the quality of particles. And chaos perturbation was utilized to overcome the search being trapped in local optimum for particle swarm optimization (PSO) algorithm. The univariate nonstationary growth model (UNGM) was used for simulation to compare the proposed algorithm with basic PF and particle swarm optimization particle filter (PSO-PF). Under the conditions of Gaussian and non-Gaussian noise, the performances of the proposed algorithm had been verified by the simulation. The results show that the performances of the number of effective particles and root mean square error (RMSE) in the algorithm are better than the performances of the basic PF and the PSO-PF algorithm. Therefore, the accuracy and tracking performance of PF are improved.

     

    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    CARPENTER J,CLIFFORD P,FEARNHEAD P.Improved particle filter for nonlinear problems[J].IEEE Proceedings-Radar Sonar and Navigation,1999,146(1):1-7.
    [2]
    佘黎煌,张石,王鸿雁.非高斯条件下基于粒子滤波的T波交替检测算法[J].电子学报,2014,42(2):223-229. SHE L H,ZHANG S,WANG H Y.A T-wave alternans detection algotithm based on particle filtering in non-Gaussian environment[J].Acta Electronica Sinica,2014,42(2):223-229(in Chinese).
    [3]
    张玲霞,刘志仓,王辉,等.非线性系统故障诊断的粒子滤波方法[J].电子学报,2015,43(3):615-619. ZHANG L X,LIU Z C,WANG H,et al.Particle filter method for fault diagnosis in nonlinear system[J].Acta Electronica Sinica,2015,43(3):615-619(in Chinese).
    [4]
    GUSTAFSSON F,BERGMAN N,FORSSELL U,et al.Particle filters for positioning,navigation,and tracking[J].IEEE Transaction on Signal Processing,2002,50(2):425-437.
    [5]
    IBARGUREN A,MAURTUA I,PEREZ M A,et al.Multiple target tracking based on particle filtering for safety in industrial robotic cells[J].Robotics and Autonomous Systems,2015,72:105-113.
    [6]
    张华,张有光,李国彦.基于混合粒子滤波的载波估计算法[J].北京亚洲成人在线一二三四五六区学报,2013,39(2):184-189. ZHANG H,ZHANG Y G,LI G Y.Carrier estimation algorithm based on novel hybrid particle filtering[J].Journal of Beijing University of Aeronautics and Astronautics,2013,39(2):184-189(in Chinese).
    [7]
    KADIRKAMANATHAN V,LI P,JAWARD M H,et al.Particle filtering based fault detection in nonlinear stochastic systems[J].International Journal of Systems Science,2002,33(4):259-265.
    [8]
    GORDON N,SALMOND D,SMITH A.Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J].IEEE Proceedings on Radar,Sonar and Navigation,1993,140(2):107-113.
    [9]
    DOUCET A,GODSILL S,ANDRIEU C.On sequential Monte Carlo sampling methods for Bayesian filtering[J].Statistics and Computing,2000,10(3):197-208.
    [10]
    FU X Y,JIA Y M.An improvement on resampling algorithm of particle filters[J].IEEE Transactions on Signal Processing,2010,58(10):5414-5420.
    [11]
    ZHAO J,LI Z Y.Particle filter based on particle swarm optimization resampling for vision tracking[J].Expert Systems with Applications,2010,37(12):8910-8914.
    [12]
    ZHAO Z S,FENG X,LIN Y Y,et al.Improved rao-blackwellized particle filter by particle swarm optimization[J].Journal of Applied Mathematics,2013(4):1-6.
    [13]
    陈志敏,薄煜明,吴盘龙,等.基于自适应粒子群优化的新型粒子滤波在目标跟踪中的应用[J].控制与决策,2013,28(2):193-200. CHEN Z M,BO Y M,WU P L,et al.Novel particle filter algorithm based on adaptive particle swarm optimization and its application to radar target tracking[J].Control and Decision,2013,28(2):193-200(in Chinese).
    [14]
    ARULAMPALAM M S,MASKELL S,GORDON N,et al.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188.
    [15]
    LORENZ E N.The essence of chaos[M].Seattle:University of Washington Press,1993:3-7.
    [16]
    WAKANO J Y,HAUERT C.Pattern formation and chaos in spatial ecological public goods games[J].Journal of Theoretical Biology,2011,268(1):30-38.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1605) PDF downloads(1253) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return