TY - GEN
T1 - Nonlinear state estimating using adaptive particle filter
AU - Zhou, Jian
AU - Pei, Fujun
AU - Zheng, Lifang
AU - Cui, Pingyuan
PY - 2008
Y1 - 2008
N2 - It is well known the standard Particle Filter has a good effect when the observation accuracy is low. However, if the observation accuracy is high, the likelihood distribution may become aiguilles-like and locate at the tail of the prior distribution curve; this will make the filter diverge. To solve the problem, a kind of Adaptive Particle Filter is proposed in this paper. The Adaptive Particle Filter has a higher filtering stability by changing the likelihood distribution according to the Statistic characteristic of the observation noise and enlarging the overlap of the prior distribution and the likelihood distribution. A simulation is developed in nonlinear and non-Gaussian Integrated Navigation System in this paper. The simulation has been done in the condition that the observation accuracy went from low to high. The simulation result indicates that the Adaptive Particle Filter has a high filtering precision and stability even if the observation accuracy is high.
AB - It is well known the standard Particle Filter has a good effect when the observation accuracy is low. However, if the observation accuracy is high, the likelihood distribution may become aiguilles-like and locate at the tail of the prior distribution curve; this will make the filter diverge. To solve the problem, a kind of Adaptive Particle Filter is proposed in this paper. The Adaptive Particle Filter has a higher filtering stability by changing the likelihood distribution according to the Statistic characteristic of the observation noise and enlarging the overlap of the prior distribution and the likelihood distribution. A simulation is developed in nonlinear and non-Gaussian Integrated Navigation System in this paper. The simulation has been done in the condition that the observation accuracy went from low to high. The simulation result indicates that the Adaptive Particle Filter has a high filtering precision and stability even if the observation accuracy is high.
KW - Adaptive particle filter
KW - Likelihood distribution
KW - Nonlinear and non-Gaussian
KW - Observation information
UR - http://www.scopus.com/inward/record.url?scp=52149108980&partnerID=8YFLogxK
U2 - 10.1109/WCICA.2008.4593892
DO - 10.1109/WCICA.2008.4593892
M3 - Conference contribution
AN - SCOPUS:52149108980
SN - 9781424421145
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 6377
EP - 6380
BT - Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th World Congress on Intelligent Control and Automation, WCICA'08
Y2 - 25 June 2008 through 27 June 2008
ER -
