改进的部分分层式粒子滤波重采样算法

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3656-3659.

改进的部分分层式粒子滤波重采样算法

曾晓辉¹,²,师奕兵²,练艺³

1. 成都信息工程学院通信工程学院, 成都 610225;
2. 电子科技大学自动化工程学院, 成都 611731
3. 摩托罗拉系统公司, 成都 610000

收稿日期:2014-07-15 修回日期:2014-08-25 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 曾晓辉
作者简介:曾晓辉(1981-),女,四川自贡人,讲师,博士研究生,主要研究方向:图像目标信息处理、粒子滤波、自动化测试测量技术及仪器；师奕兵(1964-),男,重庆人,教授,博士,主要研究方向:自动化测试测量技术及仪器;练艺(1981-),男,四川眉山人,工程师,主要研究方向:通信信息技术、数字信号处理。
基金资助:
国家自然科学基金资助项目;四川理工学院人工智能重点实验室项目

Improved partial hierarchical resampling algorithm for particle filtering

ZENG Xiaohui¹,²,SHI Yibing²,LIAN Yi³

1. College of Communication Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China;
2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731,China;
3. Motorola Solutions Incorporated Company, Chengdu Sichuan 610000, China

Received:2014-07-15 Revised:2014-08-25 Online:2014-12-01 Published:2014-12-31
Contact: ZENG Xiaohui

摘要/Abstract

摘要：

粒子滤波算法由于其处理非线性非高斯的能力优势,目前应用领域非常广泛。然而粒子滤波中存在的粒子退化、样贫等问题同样不容忽视,针对这些问题提出了一种改进的重采样粒子滤波算法。该方法借鉴了部分分层重采样和残差重采样的思路,通过对粒子权值大中小分类,在兼顾粒子多样性的情况下用不同策略分层次复制三个集合样本,从而优化了重采样算法。最后通过与经典粒子滤波重采样算法和其他部分重采样(PR)算法相比,以一维非线性跟踪模(UNG)和二维纯角度跟踪模型(BOT)两个模型的仿真结果验证了所提算法的滤波性能和有效性。

Abstract:

Particle filter is widely applied in many fields due to its ability of dealing with nonlinear and non-Gaussian problems. However, concerning some serious problems such as particle degradation and poverty in particle filtering, an improved resampling algorithm was proposed in the paper. The idea of method was based on partial stratified resampling and residual resampling, to classify particles by large, medium and small weights and replicate samples from three hierarchical sets with different strategies. The efficiency of algorithm was improved while maintaining diversity of particles. Finally through comparison with classic sequential importance sampling and resamplings and other partial resamplings, simulation results of UNG (Univariate Non-stationary Growth) and BOT (Bearings Only Tracking) models also verify the filtering performance and validity of the proposed algorithm in this paper.

中图分类号:

TN911.7

曾晓辉师奕兵练艺. 改进的部分分层式粒子滤波重采样算法[J]. 计算机应用, 2014, 34(12): 3656-3659.

ZENG Xiaohui SHI Yibing LIAN Yi. Improved partial hierarchical resampling algorithm for particle filtering[J]. Journal of Computer Applications, 2014, 34(12): 3656-3659.

参考文献

[1] GORDON N, SALMOND D. Novel approach to nonlineal and non-Gaussian Bayesian state estimations［J］. Proceedings of Institute Electric Engineering, 1993,140(2): 107-211.
[2] ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking ［J］. IEEE Transactions on Signal Processing, 2002, 50(20):174-188.
[3] LIU J, CHEN R. Sequential Monte Carlo methods for dynamic system［J］.Journal of the American Statistical Association,1998, 93(443):1032-1044.
[4] HOL J D. Resampling in particle filters［D］. Linkping: Linkping University, 2004.
[5] BOLIC M, DJURIC P M, HONG S. Resampling algorithms for particle filters: A computational complexity perspective［J］. EURASIP Journal on Advances in Signal Processing, 2004, 15: 2267-2277.
[6] LIU J S, CHEN R, LOGYINENKO T. A theoretical framework for sequential importance sampling with resampling［C］// Sequential Monte Carlo Methods in Practice. New York: Springer, 2001: 225-246.
[7] BOLIC M, DJURIC P M, HONG S. New resampling algorithms for particle filters ［C］// Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE, 2003, 2:589-592.
[8] YU J, TANG Y, XU J. Research on particle filter with adaptive resampling based on diversity measure［J］. Computer Science, 2012, 39(6):231-234. (于金霞,汤永利,许景民.基于多样性向导的自适应重采样粒子滤波研究［J］.计算机科学, 2012, 39(6):231-234.)
[9] CHEN J, YAN P, ZHANG J. Research on weight optimal combination particle filter algorithm［J］. Computer Engineeang and Applications, 2009, 45(24):33-35.(谌剑,严平,张静远,权值优化组合粒子滤波算法研究［J］. 计算机工程与应用,2009,45(24): 33-35.)
[10] YU J, TANG Y, LIU W. Research on diversity measure in particle filter［C］// Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation. Piscataway: IEEE, 2010: 1146-1149.
[11] CHANG T, LI Y, LIU Z, et al. Particle filter algorithm based on improved resampling［J］. Application Research of Computers,2013,30(3):748-750.(常天庆,李勇,刘忠仁,等.一种改进重采样的粒子滤波算法［J］. 计算机应用研究,2013,30(3):748-750.)
[12] WAN Y, WANG S. Particle filter method based on recursive Bayes model［J］. Journal of Signal Processing, 2013,29(2): 152-158.(万洋,王首勇. 递归Bayes模型粒子滤波方法［J］.信号处理,2013,29(2):152-158.)

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