|Table of Contents|

Novel Lanscape Addptive Particle Filter Algorithm Based onConvergent Particle Swarm and Its Application

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2012年05期
Page:
861-
Research Field:
Publishing date:

Info

Title:
Novel Lanscape Addptive Particle Filter Algorithm Based onConvergent Particle Swarm and Its Application
Author(s):
CHEN Zhi-min; BO Yu-ming; WU Pan-long; ZHU Kai; YIN Ming-feng
School of Automation,NUST,Nanjing 210094,China
Keywords:
particle filters; convergent particle swarm; global optimum; inertia weight; iteration times
PACS:
TP39
DOI:
-
Abstract:
In view of that the particle filter algorithm based on the particle swarm optimization(PSO-PF)is easy to trap in local optimum and has the complex calculation and slow convergence speed,anovel lanscape adaptive particle filter algorithm based on the convergent particle swarm optimization(LAPSO-PF) is proposed. This algorithm expends the source of the particle information,introducesthe inertia weight into updating formula,and limits the particles outside the searching range. Thelocal optimum and iteration times are reduced. The simulation and test are carried out in the singlevariable non-static growth model,the target tracking model and the fault detection model. The resultsshow that this algorithm reduces the local optimization and improves the velocity and precision.

References:

[1]徐茂格,宋耀良,刘力维. 基于修正卡尔曼滤波和粒子滤波的混沌信号跟踪与检测[ J]. 南京理工大学学报2007,31(4):514-517.Xu Maoge, Song Yaoliang, Liu Liwei. Chaotic signaldetection and track based on modified extendedKalman filter and particle filtering [ J]. Journal ofNanjing University of Science and Technology,2007,31(4):514-517.
[2] Gordon N,Salmond D J,Smith A F M. Novel approachto nonlinear/ non-Gaussian Bayesian state estimation[J]. Radar and Signal Processing,IEE Proceedings F,1993,140(2):107-113.
[3] Doucet A,Godsill S. On sequential Monte Carlo samplingmethods for Bayesian filtering [ J ]. Statistics andCompuring,2000,10(1):197-208.
[4] Kong A, Liu J. Sequential imputations and Bayesianmissing data problems [ J ]. Journal AmericanStatistical Association,1994,89(2):278-288.
[5] 方正,佟国峰,徐心和. 粒子群优化粒子滤波方法[J]. 控制与决策. 2007,22(3):273-277.Fang Zheng,Tong Guofeng,Xu Xinhe. Particle swarmoptimized particle filter [ J]. Control and Decision,2007,22(3):273-277.
[6] 刘利枚,蔡自兴. 粒子群优化的多机器人协作定位方法[ J]. 中南大学学报( 自然科学版),2011,42(3):682-687.Liu Limei,Cai Zixing. Multi-robot cooperative localizationbased on particle swarm optimization [ J]. Journal ofCentral South University,2011,42(3):682-687.
[7] Wang Xiangyang,Wan Wanggen,Yu Xiaoqing. Annealedparticle filter based on particle swarm optimization for ar-ticulated three-dimensional human motion tracking[J].Optical Engineering,2010,49(1).2041-2043.
[8] 李雄杰,周东华. 非线性系统测量数据丢失时的一种粒子滤波器算法[ J]. 兵工学报. 2009,30(10):1405-1408.Li Xiongjie,Zhou Donghua. A particle filter algorithm inthe presence of missing measurements for a nonlinearsystem[J]. Acta Armamentarii,2009,30(10):1405-1408.
[9] Yu Yihua,Zheng Xuanyuan. Particle filter with ant colonyoptimization for frequency offset estimation in OFDMsystems with unknown noise distribution [ J]. SignalProcessing,2011,91(5):1339-1342.
[10] Kennedy J,Eberhart R C. Particle swarm optimisation[A]. Proceedings of IEEE International Conference onNeural Networks [ C]. Perth, Australia: IEEE, 1995:1942-1948.
[11] Li Ying,Bai Bendu,Zhang Yanning. Improved particleswarm optimization algorithm for fuzzy multi-class SVM[J]. Journal of Systems Engineering and Electronics,2010,21(3):509-513.
[12] 陈策,赵春霞. 基于混沌退火粒子群优化算法的路径测试数据生成[ J]. 南京理工大学学报,2011,35(3):376-381.Chen Ce, Zhao Chunxia. Path test data generationbased on chaos anneal particle swarm optimizationalgorithm[J]. Journal of Nanjing University of Scienceand Technology,2011,35(3):376-381.
[13] Li Xiang,Liu Yu,Su Baoku. An evolutionary particlefilter based EM algorithm and its application [ J].Journal of Harbin Institute of Technology, 2010, 17(1):70-74.
[14] 叶龙,王京玲,张勤. 遗传重采样粒子滤波器[J]. 自动化学报,2007,33(8):885-887.Ye Long, Wang Jingling, Zhang Qin. Geneticresampling particle filter[J]. Acta Automatica Sinica,2007,33(8):885-887.
[15] Borden B H, Mumford M L. A statistical glint/ radarcross section target model[ J]. IEEE Trans on AES,1983,19:781-785.

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Last Update: 2012-11-26