基于果蝇优化算法改进的粒子滤波及其在目标跟踪中的应用
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摘要
针对粒子滤波算法重采样导致的样本贫化问题,提出一种基于果蝇优化思想的粒子滤波算法.该方法视粒子权值为个体适应度值,并将果蝇不断从低浓度的地方飞向高浓度的地方的觅食寻优过程引入到粒子滤波当中,驱使粒子不断向高似然区域移动,提高了粒子群的整体质量.为了解决标准果蝇优化算法易陷入早熟的问题,将遗传算法中的交叉、变异操作自适应地应用到果蝇优化算法寻优过程当中.首先通过交叉操作改善粒子分布,当果蝇优化算法陷入局部最优时,再采用柯西变异扰动,促使算法快速跳出局部极值并继续搜索全局极值.通过非线性模型仿真以及目标跟踪实验表明该算法有效提高了非线性系统状态估计精度,具有较好的稳定性,同时降低了状态估计所需的粒子数量.
A particle filter method based on fruit fly optimization algorithm is proposed to alleviate the sample impoverishment caused by resampling.When fruit flies forage,they usually fly from low concentration areas to high concentration areas efficiently and constantly.This optimum process is introduced into the particle filter to drive particles towards the high likelihood areas ceaselessly,and thus improves the overall quality of the particle swarm.Considering that the premature convergence is always associated with the fruit fly optimization algorithm,crossover and mutation operations of genetic algorithms are applied herein adaptively to keep the diversity of samples.Firstly,the particle distribution is improved by cross operation.When the algorithm falls into the local optimum,the Cauchy mutation perturbation is then used to help the fruit fly optimization algorithm jump out of the local optimal point effectively and continue searching for global extremum.The nonlinear simulations and target tracking experiments show that the proposed algorithm improves the estimation accuracy of the nolinear systems state,and it has better stability and reduces the number of particles required for state estimation at the same time.
A particle filter method based on fruit fly optimization algorithm is proposed to alleviate the sample impoverishment caused by resampling.When fruit flies forage,they usually fly from low concentration areas to high concentration areas efficiently and constantly.This optimum process is introduced into the particle filter to drive particles towards the high likelihood areas ceaselessly,and thus improves the overall quality of the particle swarm.Considering that the premature convergence is always associated with the fruit fly optimization algorithm,crossover and mutation operations of genetic algorithms are applied herein adaptively to keep the diversity of samples.Firstly,the particle distribution is improved by cross operation.When the algorithm falls into the local optimum,the Cauchy mutation perturbation is then used to help the fruit fly optimization algorithm jump out of the local optimal point effectively and continue searching for global extremum.The nonlinear simulations and target tracking experiments show that the proposed algorithm improves the estimation accuracy of the nolinear systems state,and it has better stability and reduces the number of particles required for state estimation at the same time.
引文
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[20]韩俊英,刘成忠.自适应变异的果蝇优化算法[J].计算机应用研究,2013,30(9):2641-2644.HAN J Y,LIU C Z.Fruit fly optimization algorithm with adaptive mutation[J].Application Research of Computers,2013,30(9):2641-2644.(In Chinese)
[21]HENRIQUES J F,RUI C,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2014,37(3):583-596.
[2]王法胜,鲁明羽,赵清杰,等.粒子滤波算法[J].计算机学报,2014,37(8):1679-1694.WANG F S,LU M Y,ZHAO Q J,et al.Particle filter algorithm[J].Journal of Computer Science,2014,37(8):1679-1694.(In Chinese)
[3] GUSTAFSSON F.Particle filter theory and practice with positioning applications[J].Aerospace&Electronic Systems Magazine IEEE,2010,25(7):53-82.
[4]李天成,范红旗,孙树栋.粒子滤波理论、方法及其在多目标跟踪中的应用[J].自动化学报,2015,41(12):1981-2002.LI T C,FAN H Q,SUN S D.Particle filtering:theory,approach,and application for multitarget tracking[J].Acta Automatica Sinica,2015,41(12):1981-2002.(In Chinese)
[5] GAO M,ZHANG H.Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models[J].Computers&Geosciences,2012,44(13):70-77.
[6] CREAL D.A survey of sequential Monte Carlo methods for economics and finance[J].Serie Research Memoranda,2009,31(3):245-296.
[7]朱志宇.粒子滤波算法及其应用[M].北京:科学出版社,2010:27-31.ZHU Z Y.Particle filter algorithm and its application[M].Beijing:Science Press,2010:27-31.(In Chinese)
[8] ARULAMPALAM M S,MASKELL S,GORDON N,et al.A tutorial on particle filters for online nonlinear/nongaussisan Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188.
[9] FOO P H,NG G W.Combing the interacting multiple model method with particle filters for manoeuvring target tracking[J].IET Radar,Sonar and Navigation,2011,5(3):234-255.
[10]LI T C,SATTAR T P,SUN S D.Deterministic resampling:unbiased sampling to avoid sample impoverishment in particle filters[J].Signal Processing,2012,92(7):1637-1645.
[11]程水英,张剑云.裂变自举粒子滤波[J].电子学报,2008,36(3):500-504.CHENG S Y,ZHANG J Y.Fission bootstrap particle filter[J].Journal of Electronics,2008,36(3):500-504.(In Chinese)
[12]张光,张英堂,任国全,等.基于正则化粒子滤波的磁梯度张量跟踪方法[J].探测与控制学报,2014(2):0050-0053.ZHANG G,ZHANG Y T,REN G Q,et al.Tracking method of magnetic gradient tensor based on RPF[J].Journal of Detection&Control,2014(2):0050-0053.(In Chinese)
[13]YU Y,ZHENG X.Particle filter with ant colony optimization for frequency offset estimation in OFDM systems with unknown noise distribution[J].Signal Processing,2011,91(5):1339-1342.
[14]田梦楚,薄煜明,陈志敏,等.萤火虫算法智能优化粒子滤波[J].自动化学报,2016,42(1):89-97.TIAN M C,BO Y M,CHEN Z M,et al.Firefly algorithm intelligence optimized particle filter[J].Acta Automatica Sinica,2016,42(1):89-97.(In Chinese)
[15]汪荣贵,李孟敏,吴昊,等.一种新型的基于自适应遗传算法的粒子滤波算法[J].中国科学技术大学学报,2011,41(2):134-141.WANG R G,LI M M,WU H,et al.A new particle filter algorithm based on the adaptive genetic algorithm[J].Journal of University of Science and Technology of China,2011,41(2):134-141.(In Chinese)
[16]TIAN Y,LU C,WANG Z,et al.Artificial fish swarm algorithm-based particle filter for li-ion battery life prediction[J].Mathematical Problems in Engineering,2014(3):1-10.
[17]PAN W T.A new fruit fly optimization algorithm:taking the financial distress model as an example[J].Knowledge-Based Systems,2012,26(2):69-74.
[18]韩虎.果蝇优化算法的分析[J].计算机系统应用,2017,26(2):9-17.HAN H.Analysis on fruit fly optimization algorithm[J].Journal of Computer Applications,2017,26(2):9-17.(In Chinese)
[19]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420.LZ S,HOU Z R.Particle swarm optimization with adaptive mutation[J].Acta Electronica Sinica,2004,32(3):416-420.(In Chinese)
[20]韩俊英,刘成忠.自适应变异的果蝇优化算法[J].计算机应用研究,2013,30(9):2641-2644.HAN J Y,LIU C Z.Fruit fly optimization algorithm with adaptive mutation[J].Application Research of Computers,2013,30(9):2641-2644.(In Chinese)
[21]HENRIQUES J F,RUI C,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2014,37(3):583-596.