Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Change Detection Using Non-linear Filtering and Likelihood Ratio Testing


M. Tyler, M. Morari

vol. AUT96-17

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.


Type of Publication:

(04)Technical Report

No Files for download available.
% 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}},
Permanent link