基于蝙蝠算法的粒子滤波法研究
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摘要
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,进而提髙粒子整体的质量和分布的合理性;此外,改进算法引入Levy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 Levy 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.
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 Levy 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.
引文
[1]Hossein T N,Akihiro T,Seiichi M 2012 IEEE Trans.Intell.Transp.Syst.13 748
[2]Li H W,Wang J 2012 IET Radar Sonar Navig.6 180
[3]Vasileios M,Panos S 2012 J.Comput.Phys.231 602
[4]Yang W M,Zhao M R 2016 Acta 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 2013 IEEE 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 230747
[7]Zhang Q,Qiao Y K,Kong X Y,Si X S 2014 Acta 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 2013 Acta 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 2008 Control and Decision23 117(in Chinese)[张琪,胡昌华,乔玉坤2008控制与决策23 117]
[10]Li T,Sat tar T P,Sun S 2012 Signal Process.92 1637
[11]Pawel M S,Zsofia L,Robert B 2013 Automatica 49 147
[12]Yu Y,Zheng X 2011 Signal Process.91 1339
[13]Zhong J,Fung Y F 2012 IET Control Theory Appl.6689
[14]Xian W,Long B,Li M,Wang H 2013 IEEE Trans.Instrum.Meas.63 2
[15]Liu Y L,Lin B J 2010 Control 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,L(u|")Q 2010 Control Theory&Applications 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 2014 Neural Comput.Appl.25 1369
[20]Yao Z N,Liu D M,Liu S D,Zhu X L 2014 Acta 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 LAM,Nakamura R Y M,Costa K A P,Yang X S,Souza A N 2014 Expert Syst.Appl.41 2250
[22]Chen Z M,Qu Y X,Xi Z D,Liu B,Kang D Y 2016Asian J.Control 18 1877
[2]Li H W,Wang J 2012 IET Radar Sonar Navig.6 180
[3]Vasileios M,Panos S 2012 J.Comput.Phys.231 602
[4]Yang W M,Zhao M R 2016 Acta 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 2013 IEEE 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 230747
[7]Zhang Q,Qiao Y K,Kong X Y,Si X S 2014 Acta 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 2013 Acta 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 2008 Control and Decision23 117(in Chinese)[张琪,胡昌华,乔玉坤2008控制与决策23 117]
[10]Li T,Sat tar T P,Sun S 2012 Signal Process.92 1637
[11]Pawel M S,Zsofia L,Robert B 2013 Automatica 49 147
[12]Yu Y,Zheng X 2011 Signal Process.91 1339
[13]Zhong J,Fung Y F 2012 IET Control Theory Appl.6689
[14]Xian W,Long B,Li M,Wang H 2013 IEEE Trans.Instrum.Meas.63 2
[15]Liu Y L,Lin B J 2010 Control 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,L(u|")Q 2010 Control Theory&Applications 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 2014 Neural Comput.Appl.25 1369
[20]Yao Z N,Liu D M,Liu S D,Zhu X L 2014 Acta 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 LAM,Nakamura R Y M,Costa K A P,Yang X S,Souza A N 2014 Expert Syst.Appl.41 2250
[22]Chen Z M,Qu Y X,Xi Z D,Liu B,Kang D Y 2016Asian J.Control 18 1877