搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于蝙蝠算法的粒子滤波法研究

陈志敏 田梦楚 吴盘龙 薄煜明 顾福飞 岳聪

引用本文:
Citation:

基于蝙蝠算法的粒子滤波法研究

陈志敏, 田梦楚, 吴盘龙, 薄煜明, 顾福飞, 岳聪

Intelligent particle filter based on bat algorithm

Chen Zhi-Min, Tian Meng-Chu, Wu Pan-Long, Bo Yu-Ming, Gu Fu-Fei, Yue Cong
PDF
导出引用
  • 标准粒子滤波容易出现粒子贫化问题,滤波精度不稳定,并且需要大量粒子才能对非线性系统进行准确估计,降低了算法的综合性能.针对该问题,本文提出了一种基于蝙蝠算法的新型粒子滤波算法.该算法用粒子表征蝙蝠个体,模拟蝙蝠群体搜索猎物的过程,粒子群体通过调整频率、响度、脉冲发射率,追随当前最优粒子在解空间中进行搜索,并可以动态控制全局搜索及局部搜索的相互转换,进而提高粒子整体的质量和分布的合理性;此外,改进算法引入Lvy飞行策略,从而避免局部极值的不良吸引.实验表明新型粒子滤波方法提高了粒子多样性和滤波预测精度,同时大大降低了对非线性系统进行状态预测所需的粒子数量.
    Particle filer is apt to have particle impoverishment with unstable filtering precision, and a large number of granules are required to estimate the nonlinear system accurately, which reduces the comprehensive performance of the algorithm. To solve this problem, a new particle filter based on bat algorithm is presented in this paper, where particles are used to represent individual bat so as to imitate the search process of bats for preys. In traditional resampling process, particles are directly discarded, the improved algorithm adopts another approach and solves the problem of particle impoverishment. It combines the advantages of particle swarm optimization algorithm and harmonic algorithm perfectly. New particle filter has capacity of global and local search and is superior in computation accuracy and efficiency. By adjusting frequency, loudness, and impulse emissivity of particle swarm, the optimal particle at that time is followed by particle swarm to search in the solution space. The global search and local search can be switched dynamically to improve the overall quality of the particles swarm as well as the distribution rationality. In addition, the improved particle filter uses Lvy flight strategy to avoid being attracted by harmful local optimal solution, it expands the space of research and further promotes the optimization effect of particle distribution. Using the useful information about particle swarm, improved particle filter can make particles get rid of local optimum and reduce the waste of iterations in insignificant status change. Based on the number of valid particle samples, it can improve quality of particle samples by expanding their diversity. In information interaction mechanism of improved particle filter, the method in this paper sets scoreboard of particle target function to compare the value of particle target function at each iteration sub-moment with the value of target function on scoreboard to gain global optimum of all particles at current filtering moment. Taking information interaction between global optimum and particle swarm, the guiding function of global optimum is realized. The process of particle optimization is ended prematurely through setting a maximum iteration or termination threshold. There is a tendency for the whole particle swarm closing to high likehood area without global convergence so that the advantages of improved particle filter in accuracy and speed will not be damaged. In addition, convergence analysis and computational complexity analysis are given in this paper. Experiment indicates that this method can improve the particle diversity and prediction accuracy of particle filter, and meanwhile reduce the particle quantity obviously which is required by the state value prediction for nonlinear system.
      通信作者: 陈志敏, chenzhimin@188.com
    • 基金项目: 国家自然科学基金(批准号:61501521,U1330133,61473153)和中国博士后科学基金(批准号:2015M582861)资助的课题.
      Corresponding author: Chen Zhi-Min, chenzhimin@188.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61501521, U1330133, 61473153) and the China Postdoctoral Science Foundation (Grant No. 2015M582861).
    [1]

    Hossein T N, Akihiro T, Seiichi M 2012IEEE Trans.Intell.Transp.Syst. 13 748

    [2]

    Li H W, Wang J 2012IET Radar Sonar Navig. 6 180

    [3]

    Vasileios M, Panos S 2012J.Comput.Phys. 231 602

    [4]

    Yang W M, Zhao M R 2016Acta Phys.Sin. 65 040502(in Chinese)[杨伟明, 赵美蓉2016物理学报65 040502]

    [5]

    Du M, Nan X M, Guan L 2013IEEE Trans.Image Process. 22 3852

    [6]

    Chen Z M, Qu Y X, Liu B, Fu M H, Chen J H 2016Proc.Inst.Mech.Eng.G:J.Aerosp.Engineering 230 747

    [7]

    Zhang Q, Qiao Y K, Kong X Y, Si X S 2014Acta Phys.Sin. 63 110505(in Chinese)[张琪, 乔玉坤, 孔祥玉, 司小胜2014物理学报63 110505]

    [8]

    Wang X, Han C Z 2013Acta Automatica Sinica 39 1152(in Chinese)[王晓, 韩崇昭2013自动化学报39 1152]

    [9]

    Zhang Q, Hu C H, Qiao Y K 2008Control and Decision 23 117(in Chinese)[张琪, 胡昌华, 乔玉坤2008控制与决策23 117]

    [10]

    Li T, Sattar T P, Sun S 2012Signal Process. 92 1637

    [11]

    Pawel M S, Zsfia L, Robert B 2013Automatica 49 147

    [12]

    Yu Y, Zheng X 2011Signal Process. 91 1339

    [13]

    Zhong J, Fung Y F 2012IET Control Theory Appl. 6 689

    [14]

    Xian W, Long B, Li M, Wang H 2013IEEE Trans.Instrum.Meas. 63 2

    [15]

    Liu Y L, Lin B J 2010Control and Decision 25 361(in Chinese)[刘云龙, 林宝军2010控制与决策25 361]

    [16]

    Qiu X N, Liu S R, LQ 2010Control TheoryApplications 27 1724(in Chinese)[邱雪娜, 刘士荣, 吕强2010控制理论与应用27 1724]

    [17]

    Chen Z M, Bo Y M, Wu P L, Duan W Y, Liu Z F 2013Control and Decision 28 193(in Chinese)[陈志敏, 薄煜明, 吴盘龙, 段文勇, 刘正凡2013控制与决策28 193]

    [18]

    Gandomi A H, Yang X S, Alavi A H, Talatahari S 2013Neural Comput.Appl. 22 1239

    [19]

    Li L L, Zhou Y Q 2014Neural Comput.Appl. 25 1369

    [20]

    Yao Z N, Liu D M, Liu S D, Zhu X L 2014Acta Phys.Sin. 63 227502(in Chinese)[姚振宁, 刘大明, 刘胜道, 朱兴乐2014物理学报63 227502]

    [21]

    Rodrigues D, Pereira L A M, Nakamura R Y M, Costa K A P, Yang X S, Souza A N 2014Expert Syst.Appl. 41 2250

    [22]

    Chen Z M, Qu Y X, Xi Z D, Liu B, Kang D Y 2016Asian J.Control 18 1877

  • [1]

    Hossein T N, Akihiro T, Seiichi M 2012IEEE Trans.Intell.Transp.Syst. 13 748

    [2]

    Li H W, Wang J 2012IET Radar Sonar Navig. 6 180

    [3]

    Vasileios M, Panos S 2012J.Comput.Phys. 231 602

    [4]

    Yang W M, Zhao M R 2016Acta Phys.Sin. 65 040502(in Chinese)[杨伟明, 赵美蓉2016物理学报65 040502]

    [5]

    Du M, Nan X M, Guan L 2013IEEE Trans.Image Process. 22 3852

    [6]

    Chen Z M, Qu Y X, Liu B, Fu M H, Chen J H 2016Proc.Inst.Mech.Eng.G:J.Aerosp.Engineering 230 747

    [7]

    Zhang Q, Qiao Y K, Kong X Y, Si X S 2014Acta Phys.Sin. 63 110505(in Chinese)[张琪, 乔玉坤, 孔祥玉, 司小胜2014物理学报63 110505]

    [8]

    Wang X, Han C Z 2013Acta Automatica Sinica 39 1152(in Chinese)[王晓, 韩崇昭2013自动化学报39 1152]

    [9]

    Zhang Q, Hu C H, Qiao Y K 2008Control and Decision 23 117(in Chinese)[张琪, 胡昌华, 乔玉坤2008控制与决策23 117]

    [10]

    Li T, Sattar T P, Sun S 2012Signal Process. 92 1637

    [11]

    Pawel M S, Zsfia L, Robert B 2013Automatica 49 147

    [12]

    Yu Y, Zheng X 2011Signal Process. 91 1339

    [13]

    Zhong J, Fung Y F 2012IET Control Theory Appl. 6 689

    [14]

    Xian W, Long B, Li M, Wang H 2013IEEE Trans.Instrum.Meas. 63 2

    [15]

    Liu Y L, Lin B J 2010Control and Decision 25 361(in Chinese)[刘云龙, 林宝军2010控制与决策25 361]

    [16]

    Qiu X N, Liu S R, LQ 2010Control TheoryApplications 27 1724(in Chinese)[邱雪娜, 刘士荣, 吕强2010控制理论与应用27 1724]

    [17]

    Chen Z M, Bo Y M, Wu P L, Duan W Y, Liu Z F 2013Control and Decision 28 193(in Chinese)[陈志敏, 薄煜明, 吴盘龙, 段文勇, 刘正凡2013控制与决策28 193]

    [18]

    Gandomi A H, Yang X S, Alavi A H, Talatahari S 2013Neural Comput.Appl. 22 1239

    [19]

    Li L L, Zhou Y Q 2014Neural Comput.Appl. 25 1369

    [20]

    Yao Z N, Liu D M, Liu S D, Zhu X L 2014Acta Phys.Sin. 63 227502(in Chinese)[姚振宁, 刘大明, 刘胜道, 朱兴乐2014物理学报63 227502]

    [21]

    Rodrigues D, Pereira L A M, Nakamura R Y M, Costa K A P, Yang X S, Souza A N 2014Expert Syst.Appl. 41 2250

    [22]

    Chen Z M, Qu Y X, Xi Z D, Liu B, Kang D Y 2016Asian J.Control 18 1877

  • [1] 庄杰, 韩瑞, 季振宇, 石富坤. 量化电导率模型参数多样性导致的脉冲电场消融预测的不确定性. 物理学报, 2023, 72(14): 147701. doi: 10.7498/aps.72.20230203
    [2] 郭力仁, 胡以华, 董骁, 李敏乐. 运动目标激光微多普勒效应平动补偿和微动参数估计. 物理学报, 2018, 67(15): 150701. doi: 10.7498/aps.67.20172754
    [3] 杨伟明, 赵美蓉. 自调整平滑区间粒子滤波平滑算法. 物理学报, 2016, 65(4): 040502. doi: 10.7498/aps.65.040502
    [4] 吴昊, 陈树新, 杨宾峰, 陈坤. 基于广义M估计的鲁棒容积卡尔曼滤波目标跟踪算法. 物理学报, 2015, 64(21): 218401. doi: 10.7498/aps.64.218401
    [5] 黄宇, 刘玉峰, 彭志敏, 丁艳军. 基于量子并行粒子群优化算法的分数阶混沌系统参数估计. 物理学报, 2015, 64(3): 030505. doi: 10.7498/aps.64.030505
    [6] 姚振宁, 刘大明, 刘胜道, 朱兴乐. 基于不敏粒子滤波的水中非合作磁性目标实时磁定位方法. 物理学报, 2014, 63(22): 227502. doi: 10.7498/aps.63.227502
    [7] 陈颖, 王文跃, 于娜. 粒子群算法优化异质结构光子晶体环形腔滤波特性. 物理学报, 2014, 63(3): 034205. doi: 10.7498/aps.63.034205
    [8] 张淑宁, 赵惠昌, 熊刚, 郭长勇. 基于粒子滤波的单通道正弦调频混合信号分离与参数估计. 物理学报, 2014, 63(15): 158401. doi: 10.7498/aps.63.158401
    [9] 张琪, 乔玉坤, 孔祥玉, 司小胜. 随机摄动强跟踪粒子滤波算法. 物理学报, 2014, 63(11): 110505. doi: 10.7498/aps.63.110505
    [10] 刘暾东, 陈俊仁, 洪武鹏, 邵桂芳, 王婷娜, 郑骥文, 文玉华. 基于粒子群算法的Pt-Pd合金纳米粒子的稳定结构研究. 物理学报, 2013, 62(19): 193601. doi: 10.7498/aps.62.193601
    [11] 张宏立, 宋莉莉. 基于量子粒子群算法的混沌系统参数辨识. 物理学报, 2013, 62(19): 190508. doi: 10.7498/aps.62.190508
    [12] 李盼池, 王海英, 宋考平, 杨二龙. 量子势阱粒子群优化算法的改进研究. 物理学报, 2012, 61(6): 060302. doi: 10.7498/aps.61.060302
    [13] 盛峥, 陈加清, 徐如海. 利用粒子滤波从雷达回波实时跟踪反演大气波导. 物理学报, 2012, 61(6): 069301. doi: 10.7498/aps.61.069301
    [14] 冷洪泽, 宋君强, 曹小群, 杨锦辉. 基于粒子滤波的一种改进的资料同化方法. 物理学报, 2012, 61(7): 070501. doi: 10.7498/aps.61.070501
    [15] 方伟, 孙俊, 谢振平, 须文波. 量子粒子群优化算法的收敛性分析及控制参数研究. 物理学报, 2010, 59(6): 3686-3694. doi: 10.7498/aps.59.3686
    [16] 朱志宇, 杨官校. 基于Stiefel流形的粒子滤波器研究. 物理学报, 2010, 59(12): 8316-8321. doi: 10.7498/aps.59.8316
    [17] 宁小磊, 王宏力, 张琪, 陈连华. 区间衍生粒子滤波器. 物理学报, 2010, 59(7): 4426-4433. doi: 10.7498/aps.59.4426
    [18] 高 飞, 童恒庆. 基于改进粒子群优化算法的混沌系统参数估计方法. 物理学报, 2006, 55(2): 577-582. doi: 10.7498/aps.55.577
    [19] 杜正聪, 唐 斌, 李 可. 混合退火粒子滤波器. 物理学报, 2006, 55(3): 999-1004. doi: 10.7498/aps.55.999
    [20] 李 焱, 陈建国, 李大义, 陆 洋, 周小红. 外腔半导体激光器调谐输出曲线双稳环的多样性及多解与基本参量的关系. 物理学报, 1999, 48(12): 2252-2258. doi: 10.7498/aps.48.2252
计量
  • 文章访问数:  5602
  • PDF下载量:  390
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-08-24
  • 修回日期:  2016-12-11
  • 刊出日期:  2017-03-05

/

返回文章
返回