DocumentCode :
518725
Title :
The polynomial predictive particle filter
Author :
Yin, Jian Jun ; Zhang, Jian Qiu ; Gao, Yu
Author_Institution :
Electron. Eng. Dept., Fudan Univ. Shanghai, Shanghai, China
Volume :
4
fYear :
2010
fDate :
27-29 March 2010
Firstpage :
527
Lastpage :
531
Abstract :
We firstly constructed a new dynamic state space model with little exact knowledge of the original state dynamics by using the polynomial predictive filter and state dimension extension. Then a particle filter was used to estimate the extended state, where the sum of the extended particle weights was applied to test whether the filter is convergent or not. Finally the estimate of the original state was obtained by wiping off the components corresponding to the backward time steps. Simulation results demonstrate that, for unknown state dynamics, where the existed particle filter (PF) diverges, the proposed polynomial predictive particle filter (PPPF) still works well.
Keywords :
particle filtering (numerical methods); polynomials; dynamic state space model; polynomial predictive filter; polynomial predictive particle filter; state dimension extension; unknown state dynamics; Data mining; Filtering; Kalman filters; Particle filters; Particle measurements; Polynomials; Predictive models; State estimation; State-space methods; Testing; particle filtering; polynomial predictive filter; simulation; tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
Type :
conf
DOI :
10.1109/ICACC.2010.5486865
Filename :
5486865
Link To Document :
بازگشت