吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (04): 1070-1075.doi: 10.7964/jdxbgxb201304036

• paper • Previous Articles     Next Articles

Invasive weed optimized particle filter

YANG Lan, ZHAO Xiang-mo, HUI Fei, ZHOU Jing-mei, SHI Xin   

  1. School of Information Engineering, Chang'an University, Xi'an 710064,China
  • Received:2012-06-22 Online:2013-07-01 Published:2013-07-01

Abstract:

In order to solve the sample impoverishment phenomenon, the Invasive Weed Optimization method was introduced into Sample Inspection Report (IWOSIR) of generic particle filter. This method incorporates the newest observations into the sampling process, enabling the particles to reproduce dynamically in the nearby space with their own fitness, and optimizes the particle population with optimal weights. Though IWOSIR, particles are moved towards the regions where they have larger values of posterior density. As a result, the approach relieves the effect caused by sample impoverishment of particle filter though ameliorating the diversity of sample set. Simulation results demonstrate that IWOSIR has higher estimation accuracy and operational efficiency.

Key words: information processing, nonlinear state estimation, particle filter, sample impoverishment, invasive weed optimization

CLC Number: 

  • TP391.9

[1] Blom H, Bloem E A. Exact Bayesian and particle filtering of stochastic hybrid systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 55-70.

[2] Arulampalam M S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Trans Signal Process, 2002, 50: 174-188.

[3] Georges O, Anne P, Jean D R. The particle filters and their applications[J]. Chemometrics and Intelligent Laboratory Systems, 2008,91: 87-93.

[4] Gordon N J, Maskell S, Kirubarajan T. Effcient particlefilters for joint tracking and classification [J]. Proceedings of the Signal and Data Processing of Small Targets. Orlando, USA: SPIE, 2002. 439-449.

[5] Clapp T C. Statistical methods for the processing of communication data. Cambridge: University of Cambridge, 2000:31-46.

[6] Pitt M, Shephard N. Filtering via simulation: Auxiliary particle filters[J]. J Amer Statist Assoc, 1999, 94(446):590-599.

[7] Chen Z, Haykin S. On different facets of regularization theory[J]. Neural Comput, 2002 , 14 (12) : 2791-2846.

[8] Ronghua L, Bingrong H. Coevolution based adaptive Monte Carlo localization[J]. Int J of Advanced Robotic Systems, 2004, 1(3): 183-190.

[9] 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 Guo-feng, Xu Xin-he. Paritcle swarm optimized particle filter[J]. Control and Decision, 2007, 22(3):273-277.

[10] Peter T, Csaba S. LS-N- IPS: An improvement of particle filters by means of local search//Proc Nonlinear Control Systems,Petersburg, 2001: 715-719.

[11] Mehrabian A R, Lucas C. A novel numerical optimization algorithm inspired from weed colonization[J]. Ecological Informatics, 2006, 1(3): 355-366.

[12] 苏守宝, 汪继文, 张玲, 等. 一种约束工程设计问题的入侵性杂草优化算法[J]. 中国科学技术大学学报, 2009, 39(8): 885-893. Su Shou-bao, Wang Ji-wen, Zhang Ling,et al. An invasive weed optimization algorithm for constrained engineering design problems[J]. Journal of University of Science and Technology of China, 2009, 39(8): 885-893.

[13] 胡士强,敬中良. 粒子滤波算法综述[J] . 控制与决策, 2005, 20(4) : 361-365. Hu Shi-qiang, Jing Zhong-liang. Overview of particle filter algorithm[J]. Control and Decision, 2005, 20(4):361-365.

[14] Kabaoglu N. Target tracking using particle filters with support vector regression[J]. IEEE Transactions on Vehicular Technology,2009,58(5): 2569-2573.

[15] 张氢,陈丹丹,秦仙蓉,等. 杂草算法收敛性分析及其在工程中的应用[J]. 同济大学学报:自然科学版,2010,38(11):1679-1693. Zhang Qing,Chen Dan-dan,Qin Xian-rong,et al. Convergence analysis of invasive weed optimization algorithm and its application in engineering[J]. Journal of TongJi University (Natural Science), 2010,38 (11):1679-1693.

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