Title :
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
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;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305775
