交互式多区域模型

doi:10.3969/j.issn.0372-2112.2012.06.028

电子学报

交互式多区域模型

曲彦文^1,2, 张二华², 杨静宇²

1. 江西师范大学计算机信息工程学院,江西南昌 330000;
2. 南京理工大学计算机科学与技术学院,江苏南京 210094

收稿日期:2011-02-18 修回日期:2011-09-10 出版日期:2012-06-25
- 作者简介:
- 曲彦文 男,1983年4月出生于江西省南昌市.现为江西师范大学讲师,从事信号处理、数据融合方面的研究. E-mail:earverse@hotmail.com
  张二华 男,1967年3月出生于湖北省武孝感市.现为南京理工大学副教授,从事科学可视化、信号处理方面的研究. E-mail:zherhua@163.com
- 基金资助:
- 国家自然科学基金 (No.90820306)

Interacting Multiple Region Model

QU Yan-wen^1,2, ZHANG Er-hua², YANG Jing-yu²

1. School of Computer Information and Engineer,Jiangxi Normal University,Nanchang,Jiangxi 330000,China;
2. School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing,Jiangsu 210094,China

Received:2011-02-18 Revised:2011-09-10 Online:2012-06-25 Published:2012-06-25
- Supported by:
- National Natural Science Foundation of China (No.90820306)

摘要/Abstract

摘要： 一种被称为交互式多区域模型(IMRM)的非线性滤波算法被提出,用于对状态和连续系统参数进行联合估计.IMRM将连续的系统参数空间视为由若干子区域所构成的集合,并将每个子区域分别分配给一个子模型.IMRM使用一组子滤波器并行滤波.在每一时刻,IMRM利用交互操作计算各子模型的混合初始化环境,之后各子滤波器在假设系统参数跳变到特定子区域的前提下,对状态和系统参数进行估计.为了有效地应用IMRM,提出了一种基于无迹变换的交互式多区域模型(UT-IMRM)算法.UT-IMRM对每个子模型使用无迹卡尔曼滤波器(UKF)进行滤波.在目标跟踪实验中对UT-IMRM性能进行测试.实验结果显示当系统参数不属于IMM模型集合时,UT-IMRM能够比IMM获得更好的估计性能.

关键词: 交互式多模型, 参数估计, 无迹卡尔曼滤波器

Abstract: A nonlinear filtering method called Interacting Multiple Region Model (IMRM) is proposed to estimate the state and continuous system parameter together.IMRM regards the continuous system parameter space as a set of disjoint sub-regions,and each sub-region is assigned to a sub-model respectively.IMRM runs a bank of sub-filters in parallel.At each time step,IMRM computes the mixed initial condition for each sub-model by interaction operation,and each sub-filter estimates the state and system parameter on the condition that the system parameter belongs to a unique sub-region.In order to implement the IMRM efficiently,Unscented Transformation based IMRM (UT-IMRM) is developed by using the unscented kalman filter as the sub-filter.A target tracking experiment is designed to test the performance of UT-IMRM.Experimental results show that UT-IMRM achieves better estimation performance than that of IMM when the system parameter doesn’t belong to the IMM model set.

Key words: interacting multiple model, parameter estimation, unscented Kalman filter

中图分类号:

TP301.6

曲彦文, 张二华, 杨静宇. 交互式多区域模型[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2012.06.028.

QU Yan-wen, ZHANG Er-hua, YANG Jing-yu . Interacting Multiple Region Model[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2012.06.028.

参考文献

[1] van der MERMER R,WAN E A.The square-root unscented Kalman filter for state and parameter estimation .Proc of the 2001 IEEE International Conf on Acoustics,Speech and Signal Processing-Proceedings .Salt Lake City:IEEE Press,2001.3461-3464.

[2] GORDON N,SALMOND D,SMITH A F.Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J].IEE Proceedings Part F Radar and Signal Processing,1993,140(2):107-113.

[3] POYIADJIS G,SINGH S S,DOUCET A.Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models .Proc of the 2006 IEEE International Conf on Acoustics,Speech,and Signal Processing .Toulouse:IEEE Press,2006.III.329-III.332.

[4] Kitagawa G.A self-organizing state-space model[J].Journal of American Statistical Association,1998,93:1203-1215.

[5] LIU J,WEST M.Combined parameter and state estimation in simulation-based filtering .Doucet A,de Freitas N,Gordon N,et al.Sequential Monte Carlo in Practice .New York:Springer-Verlag,2001.197-223.

[6] CHEN T,MORRIS J,MARTIN E.Particle filters for state and parameter estimation in batch processes[J].Journal of Process Control,2005,15(6):665-673.

[7] CUI N Z,HONG L,LAYNE J R.A comparison of nonlinear filtering approaches with an application to ground target tracking[J].Signal Processing,2005,85(8):1469-1492.

[8] 刘先省,胡振涛,金勇,杨一平.基于粒子优化的多模型粒子滤波算法[J].电子学报,2010,32(2):301-306. LIU Xian-xing,HU Zhen-tao,JIN Yong,YANG Yi-ping.A novel multiple model particle filter algorithm based on particle optimization[J].Acta Electronica Sinica,2010,38(2):301-306.(in Chinese)

[9] BLOM H A P,BAR-Shalom Y.The interacting multiple model algorithm for systems with Markovian switching coefficients[J].IEEE Transcations on Automatic Control,1988,33(8):780-783.

[10] ZHONG Z W,MENG H D,WANG X Q.Extended target tracking using an IMM based rao-blackwellised unscented Kalman Filter .Proc of the 2008 9th International Conf on Signal Processing .Beijing:IEEE Press,2008.2409-2412.

[11] 胡振涛,潘泉,杨峰.基于广义UT变换的交互式多模型粒子滤波算法,电子学报[J].2010,38(6):1443-1448. HU Zhen-tao,PAN Quan,Yang Feng.Interacting multiple model particle filtering algorithm based generalized unscented transformation[J].Acta Electronica Sinica,2010,38(6):1443-1448.(in Chinese)

[12] 张俊根,姬红兵.IMM迭代扩展卡尔曼粒子滤波跟踪算法[J].电子与信息学报,2010,32(5):1116-1120. ZHANG Jun-gen,JI Hong-bing.IMM iterated extended Kalman particle filter based target tracking[J].Journal of Electronics&Information Technology,2010,32(5):1116-1120.(in Chinese)

[13] van der MERMER R,Doucet A,de Freitas N.The Unscented Particle Filter .Cambridge:University of Cambridge,2000.

[14] DUDA R O,HART P E,STORK D G.Pattern Classification Second Edition[M].Beijing:Machine Press,2004.

[15] LI X R.A Survey of maneuvering target tracking.partⅠdynamic models[J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1333-1364.

交互式多区域模型

Interacting Multiple Region Model

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