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Change Detection Using Non-linear Filtering and Likelihood Ratio Testing

Author(s):

M. Tyler, M. Morari
Conference/Journal:

vol. AUT96-17
Abstract:

This article presents a framework for general change detection problems. A two-model approach is used, wherein signals and parameters subject to change are modeled by Brownian motion for the faulty case and by constant values in the nominal case. A detection algorithm using likelihood ratio testing is implemented through the use of recursive dynamic filtering. In the case of change in mean of a Gaussian sequence, a detailed analysis of the detection scheme reveals that for fixed error rates, there exist optimal filtering parameters which optimize the detection rate. For non-linear and non-Gaussian change detection, approximate filtering algorithms based on Bayes' law can be employed in the present framework. A computational filtering algorithm based on Bayes' law, probability grid filtering, is reviewed. The proposed framework combined with probability grid filtering is compared to the local asymptotic approach through a non-linear dynamical example. The proposed method's performance is vastly superior to the latter's.

Year:

1996
Type of Publication:

(04)Technical Report
Supervisor:



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% Autogenerated BibTeX entry
@TechReport { TylMor:1996:IFA_1502,
    author={M. Tyler and M. Morari},
    title={{Change Detection Using Non-linear Filtering and Likelihood
	  Ratio Testing}},
    institution={},
    year={1996},
    number={},
    address={},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=1502}
}
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