Compressed Iterative Particle Filter for Target Tracking

Hong Bo Zhu; Hai Zhao; Dan Liu; Chun He Song

doi:10.4028/www.scientific.net/AMM.55-57.91

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 55-57Compressed Iterative Particle Filter for Target...

Compressed Iterative Particle Filter for Target Tracking

Abstract:

Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep the variety of the particles. Simuational results have shown that the proposed algorithm has better performances than other three typical filtering algorithms.

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Periodical:

Applied Mechanics and Materials (Volumes 55-57)

Pages:

91-94

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.55-57.91

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Online since:

May 2011

Authors:

Hong Bo Zhu, Hai Zhao, Dan Liu, Chun He Song

Keywords:

Filtering Method, Genetic Algorithm (GA), Particle Filtering, Target Tracking, Unscented Particle Filter

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References

[1] Julier S J, Uhlmann J K. A general method for approximating nonlinear transformation of probability distributions [EB/OL], http: /www. robots. ox. ac. uk /~siju/work/publications /Unscented. zip, 1997-09-27.

Google Scholar

[2] ZhiQiangHou, ChongZhaoHan. Based on the background pixel classification algorithm of reconstructing [J], Journal of software, 2005, 16 (9) : 1568-1576.

Google Scholar

[3] Pitt M K, Shephard N. Filtering via simulation: Auxiliary particle filters [J]. Journal of the American Statistical Association, 1999, 94(2): 590-599.

DOI: 10.1080/01621459.1999.10474153

Google Scholar

[4] Berzuini C, Best N. Dynamic conditional independence models and Markov chain Monte Carlomethods[J], J of the American Statistical Association, 1997, 92(5): 1403-1412.

DOI: 10.1080/01621459.1997.10473661

Google Scholar

[5] Belviken E, Acklam P J. Monte Carlo filters for non-linear state estimation[J], Automatica, 2001, 37(1): 177-183.

DOI: 10.1016/s0005-1098(00)00151-5

Google Scholar

[6] Doucet A, Gordon N. Sequential Monte Carlo methods in practice[M], New York: Springer-Verlag, (2001).

Google Scholar

[7] Lei Lei, Yan-jun Li, Ke Zhang. A Fast Algorithm for Searching Object Centriods in Binary Images [J], Infrared Technology, 2007, 29(9): 548-551.

Google Scholar

[8] Higuchi T. Monte Carlo filtering using genetics algorithm operator [J], J of Statistical Computation and Simulation, 1997, 59(1): 1-23.

Google Scholar

[9] Mo Y W, Xiao D Y. Hybrid system monitoring and diagnosing based on particle filter algorithm [J], Acta Automation Sinica, 2003, 29(3): 641-648.

Google Scholar

[10] Freitas J F G, Niranjan M, Gee A H, et al. Sequential Monte Carlo Methods to Train Neural Network Models[J], Neural Computation, 2000, 12(4): 995-993.

DOI: 10.1162/089976600300015664

Google Scholar