DocumentCode :
574377
Title :
A resilient Extended Kalman Filter for discrete-time nonlinear stochastic systems with sensor failures
Author :
Xin Wang ; Yaz, Edwin E. ; Chung Seop Jeong ; Yaz, Yvonne I.
Author_Institution :
OIT EE Dept., Klamath Falls, OR, USA
fYear :
2012
fDate :
27-29 June 2012
Firstpage :
4783
Lastpage :
4788
Abstract :
Missing sensor data is a common problem which severely influences the overall performance of today´s dataintensive applications. In order to address this important issue, a resilient Extended Kalman Filter is proposed for discrete-time nonlinear stochastic system and measurement equations with sensor failures and random gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. A generalized Extended Kalman Filter is designed to have robustness against sensor failures and resilience against random perturbations in the filter gain. Lorenz oscillator, a benchmark nonlinear chaotic system, is used to demonstrate the effectiveness and resilience of the proposed approach.
Keywords :
Kalman filters; discrete time systems; nonlinear systems; stochastic systems; Lorenz oscillator; discrete-time nonlinear stochastic systems; failure rates; filter gain; generalized extended Kalman filter; measurement equations; nonlinear chaotic system; random gain perturbations; random perturbations; resilient extended Kalman filter; sensor failures; Equations; Kalman filters; Mathematical model; Noise measurement; Oscillators; Phase measurement; Pollution measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2012
Conference_Location :
Montreal, QC
ISSN :
0743-1619
Print_ISBN :
978-1-4577-1095-7
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2012.6314962
Filename :
6314962
Link To Document :
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