Distributed Analytical Graph Identification

Sundeep Prabhakar Chepuri, Mario Coutino, Antonio G. Marques, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages4064-4068
Number of pages5
ISBN (Electronic)978-1-5386-4658-8
ISBN (Print)978-1-5386-4659-5
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Distributed graph-spectral decomposition
  • Graph signal processing
  • Spectrum analysis
  • System identification
  • Topology identification

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