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Parameter-invariant detection of unknown inputs in networked systems
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2013 (English)In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), IEEE conference proceedings, 2013, 4379-4384 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work considers the problem of detecting unknown inputs in networked systems whose dynamics are governed by time-varying unknown parameters. We propose a strategy in opposition to the commonly employed approach of first estimating the unknown parameters and then using the estimates as the true parameter values for detection, e.g. maximum-likelihood approaches. The suggested detection scheme employs test statistics that are invariant to the unknown parameters and do not rely on parameter estimation. We specifically consider the case of severe lack of prior knowledge, i.e., the problem of detecting unknown inputs when nothing is known of the system but some primitive structural properties, namely that the system is a linear network, subject to Gaussian noise, and that a certain input signal is either present or not. The aim is thus to analyze the structure and performances of invariant tests in a limiting case, specifically where the amount of prior information is minimal. The developed test is proven to be maximally invariant to the unknown parameters and Uniformly Most Powerful Invariant (UMPI). Simulation results indicate that for arbitrary networked systems the parameterinvariant detector achieves a specified probability of false alarm while ensuring that the probability of detection is maximized.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 4379-4384 p.
Series
IEEE Conference on Decision and Control. Proceedings, ISSN 0743-1546
Keyword [en]
Hypothesis testing, Invariant tests, Linear systems, Networked systems, Time varying systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-138331DOI: 10.1109/CDC.2013.6760563ISI: 000352223505002Scopus ID: 2-s2.0-84902324315ISBN: 978-146735717-3 (print)OAI: oai:DiVA.org:kth-138331DiVA: diva2:680899
Conference
52nd IEEE Conference on Decision and Control, CDC 2013; Florence; Italy; 10 December 2013 through 13 December 2013
Note

QC 20140204

Available from: 2013-12-18 Created: 2013-12-18 Last updated: 2015-12-08Bibliographically approved

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Johansson, Karl Henrik

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