State-space based network topology identification

Conference Paper (2020)
Author(s)

M.A. Coutiño (TU Delft - Signal Processing Systems)

E. Isufi (TU Delft - Multimedia Computing)

Takanori Maehara (RIKEN; Tokyo)

GJT Leus (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2020 Mario Coutino, E. Isufi, T. Maehara, G.J.T. Leus
DOI related publication
https://doi.org/10.23919/Eusipco47968.2020.9287692
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Mario Coutino, E. Isufi, T. Maehara, G.J.T. Leus
Research Group
Signal Processing Systems
Pages (from-to)
1055-1059
ISBN (electronic)
978-9-0827-9705-3
Reuse Rights

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Abstract

In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional network control theory and signal processing on networks. In addition, it provides theoretical guarantees for the recovery of the topological structure of a deterministic linear dynamical system from input-output observations even though the input and state evolution networks can differ.

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