Distributed Analytical Graph Identification

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Distributed Analytical Graph Identification

Conference Paper (2018)

Author(s)

Sundeep Prabhakar Chepuri (TU Delft - Signal Processing Systems)

Mario Coutino (TU Delft - Signal Processing Systems)

Antonio G. Marques (King Juan Carlos University)

Geert Leus (TU Delft - Signal Processing Systems)

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

DOI related publication

https://doi.org/10.1109/ICASSP.2018.8461484 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:ccb53832-4f09-4e4b-a5cc-0fd7c7c3b62b

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Publication Year

2018

Language

English

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.

Article number

8461484

Pages (from-to)

4064-4068

Publisher

IEEE

ISBN (print)

978-1-5386-4659-5

ISBN (electronic)

978-1-5386-4658-8

Event

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 (2018-04-15 - 2018-04-20), Calgary Telus Convention Center (CTCC), Calgary, Canada

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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.

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