NDLI: A resilient Extended Kalman Filter for discrete-time nonlinear stochastic systems with sensor failures

Content Provider	IEEE Xplore Digital Library
Author	Xin Wang Yaz, E.E. Chung Seop Jeong Yaz, Y.I.
Copyright Year	2012
Description	Author affiliation: Math Dept., MSOE, WI, USA (Yaz, Y.I.) \|\| ECE Dept., Marquette Univ., Milwaukee, WI, USA (Yaz, E.E.; Chung Seop Jeong) \|\| OIT EE Dept., Klamath Falls, OR, USA (Xin Wang)
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.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	4783
Ending Page	4788
File Size	285519
Page Count	6
File Format	PDF
ISBN	9781457710957
ISSN	07431619
e-ISBN	9781457710964
e-ISBN	9781467321020
DOI	10.1109/ACC.2012.6314962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Pollution measurement Mathematical model Noise measurement Equations Oscillators Phase measurement Kalman filters
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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