Topology identification under spatially correlated noise

Mishfad Shaikh Veedu, Murti V. Salapaka

Research output: Contribution to journalArticlepeer-review

Abstract

This article addresses the problem of reconstructing the topology of a network of agents interacting via linear dynamics, while being excited by exogenous stochastic sources that are possibly correlated across the agents, from time-series measurements alone. It is shown, under the assumption that the correlations are affine in nature, such network of nodal interactions is equivalent to a network with added agents. The added agents are represented by nodes that are latent, where no corresponding time-series measurements are available; however, here all the exogenous excitements are spatially (that is, across agents) uncorrelated. Generalizing affine correlations, it is shown that, under polynomial correlations, the latent nodes in the expanded networks can be excited by clusters of noise sources, where the clusters are uncorrelated with each other. The clusters can be replaced with a single noise source if the latent nodes are allowed to have non-linear interactions. Finally, using the sparse plus low-rank matrix decomposition of the imaginary part of the inverse power spectral density matrix (IPSDM) of the time-series data, the topology of the network is reconstructed. Under non conservative assumptions, the correlation graph of the noise sources is retrieved.

Original languageEnglish (US)
Article number111182
JournalAutomatica
Volume156
DOIs
StatePublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Latent nodes
  • Learning and control
  • Linear dynamical systems
  • Network topology identification
  • Power spectral density
  • Probabilistic graphical model
  • Sensor placement
  • Sparse estimation
  • Structure learning
  • Time-series analysis

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