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
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;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6021911
