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

• 论文 • 上一篇    下一篇

入侵式野草优化粒子滤波方法

杨澜, 赵祥模, 惠飞, 周经美, 史昕   

  1. 长安大学 信息工程学院,西安 710064
  • 收稿日期:2012-06-22 出版日期:2013-07-01 发布日期:2013-07-01
  • 作者简介:杨澜(1985-),女,博士研究生.研究方向:多源信息融合与嵌入式控制.E-mail:yanglan.1985@163.com
  • 基金资助:

    "863"国家高技术研究发展计划项目(2009AA11Z203);国家自然科学基金项目(51278058);长安大学中央高校基本科研业务费专项项目(CHD2011ZY001).

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

摘要:

针对粒子滤波存在的样本集贫化现象,将入侵式野草优化思想融入粒子滤波的采样阶段,提出了一种入侵式野草优化粒子滤波方法。该方法通过优化采样过程并融合最新观测值,使粒子以自身权值在附近搜索空间动态繁衍,优胜劣汰出具有最优权值的粒子集,以指导粒子向后验概率的局部高似然域运动,增加了样本多样性而缓解样本集贫化现象。试验结果表明,该方法具有较高估计精度与运行效率。

关键词: 信息处理技术, 非线性状态估计, 粒子滤波, 样本集贫化, 入侵式野草优化方法

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

中图分类号: 

  • 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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