DocumentCode :
3289674
Title :
A Federated particle filtering algorithm based on EKPF
Author :
Meng, Li ; Jia-hong, Liang
Author_Institution :
Coll. of Mech. Eng. & Autom., Nat. Univ. of Defence Technol., Changsha, China
fYear :
2011
fDate :
15-17 April 2011
Firstpage :
2873
Lastpage :
2876
Abstract :
To solve the problem of information fusion in the system of nonlinear/non-Gaussian error models, a new algorithm called federated EKPF (FEKPF) algorithm was proposed based on EKPF and federated Kalman filter. In this algorithm, EKPF served as the local filter, whereas the master filter adopt the information fusion algorithm to obtain the global state estimations which are transmitted to each local filter to update particles as the feedbacks according to information distribution. The proposed algorithm was tested in subsequent simulation by contrast to FEKF. The results showed that FEKPF is more effective for nonlinear/non-Gaussian systems.
Keywords :
Kalman filters; nonlinear filters; particle filtering (numerical methods); sensor fusion; state estimation; EKPF; extended Kalman particle filter; federated Kalman filter; federated particle filtering algorithm; global state estimations; information distribution; information fusion problem; master filter; nonGaussian error models; nonlinear error models; Bayesian methods; Filtering algorithms; Global Positioning System; Information filters; Kalman filters; Particle filters; extended Kalman particle filter; federated extended Kalman filter; information fusion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
Type :
conf
DOI :
10.1109/ICEICE.2011.5778115
Filename :
5778115
Link To Document :
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