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随机摄动强跟踪粒子滤波算法

张琪 乔玉坤 孔祥玉 司小胜

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随机摄动强跟踪粒子滤波算法

张琪, 乔玉坤, 孔祥玉, 司小胜

Study on stochastic perturbation strong tracking particle filter

Zhang Qi, Qiao Yu-Kun, Kong Xiang-Yu, Si Xiao-Sheng
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  • 如何解决粒子的退化问题和提高算法对突变状态的跟踪能力,是粒子滤波算法研究和应用中需要考虑的两个主要因素. 传统的再采样算法虽然可以解决退化问题,但是容易导致粒子耗尽;扩展粒子滤波算法虽然可在一定程度上解决粒子耗尽问题,但其对突变状态的跟踪能力却不近人意;强跟踪粒子滤波算法可以提高对突变状态的跟踪能力,但却未能较好地改善粒子退化问题. 针对上述问题,本文将随机摄动再采样方法引入强跟踪粒子滤波算法,提出了一种随机摄动强跟踪粒子滤波算法. 当粒子退化问题严重时,对权值最大的粒子迭加随机摄动,用摄动粒子替换退化粒子以解决粒子退化问题,同时由于摄动粒子的加入增加了粒子集的多样性,可在一定程度上缓解粒子耗尽问题,提高算法对突变状态的跟踪能力. 利用标准验证模型和分时恒定系统对所提出的算法进行了仿真验证,仿真结果证明了该算法的可行性和有效性.
    To solve the degeneracy phenomenon and to improve the ability for tracking the breaking states are two difficult problems in the application of particle filter. Sequential important re-sampling can reduce orilliminate degeneracy, but the sample impoverishment is a secondary result. Extended particle filter can also reduce the degeneracy, but it cannot track the breaking states. The ability to track the breaking states can be improved by a strong tracking particle filter, but the degeneracy phenomenon will not be well solved still. A stochastic perturbation strong tracking particle filter is proposed for solving the above problems, in which a stochastically perturbative re-sampling is introduced into a strong tracking particle filter. Thus a stochastic perturbation is added to the particle with maximal weight to form some new particles, and the degenerative particles are displaced by the new particles to solve the degeneracy phenomenon and so the sample impoverishment improves the diversity of the samples. The ability of the proposed algorithm to track breaking states is also improved, and the feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the system with constants in different periods of time.
    • 基金项目: 国家自然科学基金(批准号:61104223,61174030,61374120)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61104223, 61174030, 61374120).
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  • [1]

    Yang Z L, Wang W W, Yin Z X, Zhang J, Chen X 2007 Chin Phys. Lett. 24 1170

    [2]

    Liu W D, Ren K F, Meunier-Guttin-Cluzel S, Gouesbet G 2003 Chin Phys. 12 1366

    [3]

    Gring A, Ristic B, Mihaylova L 2012 IEEE Transactions on Signal Processing 60 2138

    [4]

    Ghirmai T, Bugallo M F, Joaquín Míguez, Djurić P M 2005 IEEE Transaction on Signal Processing 53 2855

    [5]

    Cheng Chang, Rashid Ansari 2005 IEEE Signal Processing Letters 12 242

    [6]

    Jie Yu 2012 Journal of Process Control. 22 778

    [7]

    Djurić P M, Kotecha J H, Zhang J Q, Huang Y F, Ghirmai T, Bugallo M F, Joaquin Miguez 2003 IEEE Signal Processing Magazine. 20 19

    [8]

    Ning X L, Wang H L, Zhang Q, Chen L H 2010 Acta Phys. Sin. 59 4426 (in Chinese) [宁小磊, 王宏力, 张琪, 陈连华 2010 物理学报 59 4426]

    [9]

    Hou J, Jing Z R, Yang Y 2013 Journal of Electronics m& Information Technology 35 1532 (in Chinese)[侯静, 景占荣, 羊彦 2013 电子与信息学报 35 1532]

    [10]

    De Freitas 2000 Neural Computation. 12 955

    [11]

    Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric Wan 2000 Technical Report CUED/F-INFENG/TR 380

    [12]

    Hu C H, Zhang Q, Qiao Y K 2008 Acta Automatic Sinica 12 1522 (in Chinese)[胡昌华, 张琪, 乔玉坤 2008 自动化学报 12 1522]

    [13]

    Zhou D H, Ye Y Z 2000 Modern Fault Diagnosis and Fault Tolerant Control (Beijing: Tsinghua university press), p265-267 (in Chinese) [周东华, 叶银忠 2000 现代故障诊断与容错控制(北京: 清华大学出版社) 第265-267页]

    [14]

    Sanjeev Arulampalam, Simon Maskell, Neil Gordon 2002 IEEE Transaction on Signal Processing 50 174

    [15]

    Mo Y W, Xiao D Y 2005 Control Theory & Application 22 269 (in Chinese) [莫以为, 萧德云 2005 控制理论与应用 22 269]

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出版历程
  • 收稿日期:  2013-12-22
  • 修回日期:  2014-03-12
  • 刊出日期:  2014-06-05

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