Open AccessThe hybrid annealed particle filter
Author(s) -
Du Zheng-Cong,
Bin Tang,
Ke Li
Publication year - 2006
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.55.999
Subject(s) - particle filter , auxiliary particle filter , ensemble kalman filter , gaussian , kalman filter , computer science , extended kalman filter , algorithm , nonlinear system , importance sampling , nonlinear filter , filter (signal processing) , statistical physics , statistics , filter design , mathematics , physics , artificial intelligence , monte carlo method , quantum mechanics , computer vision
In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on-line estimation of non-Gaussian nonlinear systems. In this filtering method, state parameters separation and an annealing parameter are used to produce importance function. Since the distribution function makes full use of the prior, likelihood, and statistical characteristics of noise and the newest observation data, it is much closer to posterior distributions. Theoretical analysis and simulation show that the performance of proposed particle filter outperforms the standard particle filter and the extended Kalman filter.