NDLI: A variable geometric state filtering for stochastic linear systems subject to intermittent unknown inputs

Content Provider	IEEE Xplore Digital Library
Author	Keller, J.-Y. Sauter, D.
Copyright Year	2010
Description	Author affiliation: Nancy Université CRAN CNRS UMR 7039, BP 239, 54506 Vandoeuvre les Nancy, France (Keller, J.-Y.; Sauter, D.)
Abstract	In this paper, a new approach for state filtering of dynamic stochastic discrete-time systems affected by unknown inputs is presented. The proposed state filtering scheme includes a restricted diagonal detection filter generating a set of minimum variance white detection signals, each of them sensitive to a particular component of the unknown input vector. After having tested the statistical effect of each unknown input in order to update online the unknown inputs decoupling constraint, the variable geometric state filtering is obtained by minimizing the state estimation errors covariance matrix. Compared to the standard unknown input Kalman filter, a new degree of freedom appears in the covariance optimisation problem at the detection time of a non significant unknown input. A comparative study with the standard unknown input Kalman filter shows the efficiency of the proposed approach, particularly when the unknown inputs are intermittent.
Starting Page	558
Ending Page	563
File Size	650025
Page Count	6
File Format	PDF
ISBN	9781424481538
e-ISBN	9781424481545
DOI	10.1109/SYSTOL.2010.5676048
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intermittent unknown inputs Detection filter Stochastic processes Bank of chi-squared tests Filtering theory Covariance matrix Kalman filters State estimation Optimization
Content Type	Text
Resource Type	Article

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