DocumentCode
553939
Title
A strong tracking particle filter for state estimation
Author
Xiaolong Deng ; JinJun Lu ; Rui Yue ; Jianlin Zhang
Author_Institution
Dept. of Electr. Eng., Jiangsu Coll. of Inf. Technol., Wuxi, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
56
Lastpage
60
Abstract
One of the algorithmic cores of particle filter (PF) is the proposal distribution. A new proposal distribution combining the unscented Kalman filter (UKF) with strong tracking filter (STF) is presented. The scaling factor is added and is acquired by the techniques in the STF. It can be tuned to make the algorithm reliable and adaptive. In the nonlinear state estimation experiments, the results confirm the efficiency of the improved PF algorithm.
Keywords
Kalman filters; nonlinear estimation; particle filtering (numerical methods); state estimation; nonlinear state estimation experiments; proposal distribution; scaling factor; strong particle tracking filter; unscented Kalman filter; Filtering theory; Monte Carlo methods; Particle filters; Particle measurements; Proposals; State estimation; STF; UKF; particle filter; proposal distribution; state estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6021911
Filename
6021911
Link To Document