An Improved Particle Filtering Algorithm for Information Acquisition

Author

Li, Jingxi ; Wang, Shuzong ; Chen, Huadong

Author_Institution

Inst. for Applic. Study to Modern Technol. of Naval Weapon, Naval Univ. of Eng., Wuhan

fYear

2006

fDate

20-23 Aug. 2006

Firstpage

567

Lastpage

571

Abstract

In this paper, we present an improved particle filtering algorithm called GMPF for nonlinear, non-Gaussian and non-stationary state estimation problems in information acquisition field. The proposed algorithm integrates various virtues of current prevalent particle filters, and has satisfying filtering accuracy and numerical stability at acceptable computational cost. Simulation results show the feasibility and efficiency of the proposed algorithm compared with other related algorithms.

Keywords

Gaussian processes; Markov processes; Monte Carlo methods; numerical stability; particle filtering (numerical methods); GMPF algorithm; Gaussian mixture-unscented distribution-Markov chain Monte Carlo-particle filter; information acquisition; nonGaussian state estimation; nonlinear state estimation; nonstationary state estimation; numerical stability; Bayesian methods; Computational modeling; Filtering algorithms; Information filtering; Information filters; Monte Carlo methods; Particle filters; Proposals; Signal processing algorithms; State estimation; Gaussian mixture model; Markov Chain Monte Carlo; information acquisition; particle filter; unscented Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2006 IEEE International Conference on

Conference_Location

Shandong

Print_ISBN

1-4244-0528-9

Electronic_ISBN

1-4244-0529-7

Type

conf

DOI

10.1109/ICIA.2006.305775

Filename

4097719