State-space based network topology identification

Conference paper (2020)

Authors

Mario Coutino Signal Processing Systems - EEMCS
Takanori Maehara RIKEN; Tokyo
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Mario Coutino, E. Isufi, T. Maehara, G.J.T. Leus
DOI: https://doi.org/10.23919/Eusipco47968.2020.9287692
Graph signal processing Topology identification State-space models Signal processing over networks
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Published Date

01-08-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.