Topology Learning in Radial Dynamical Systems With Unreliable Data

Venkat Ram Subramanian, Deepjyoti Deka, Saurav Talukdar, Andrew Lamperski, Murti Salapaka

Research output: Contribution to journalArticlepeer-review


Many complex engineering systems admit bidirectional and linear couplings between their agents. Blind and passive methods to identify such influence pathways/couplings from data are central to many applications. However, dynamically related data streams originating at different sources are prone to corruption caused by asynchronous time stamps of different streams, packet drops, and noise. Such imperfect information may be present in the entire observation period and, hence, is not detected by change detection algorithms that require an initial clean observation period. In this article, we provide a novel approach to detect the location of corrupt agents as well as present an algorithm to learn the structure of radial dynamical systems despite corrupted data streams. In particular, we show that our approach provably learns the true radial structure if the unknown corrupted nodes are at least three hops away from each other. Our theoretical results are further validated in a test dynamical network.

Original languageEnglish (US)
Pages (from-to)2010-2021
Number of pages12
JournalIEEE Transactions on Control of Network Systems
Issue number4
StatePublished - Dec 1 2023

Bibliographical note

Publisher Copyright:


  • Fault detection
  • network topology
  • time series analysis
  • uncertain systems


Dive into the research topics of 'Topology Learning in Radial Dynamical Systems With Unreliable Data'. Together they form a unique fingerprint.

Cite this