Identifying radio frequency interference with hidden Markov models

Daniel Czech, Amit Kumar Mishra, Michael Inggs

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


Radio frequency interference (RFI) is a significant concern for radio astronomy. Identifying unintentional RFI signals (for example, from equipment operating in the vicinity of radio telescopes) is a challenging topic due to the highly non-ergodic nature of such signals. Another non-ergodic signal type which has been very well researched is human speech, for which hidden Markov model-based approaches have led to some of the best performing classification algorithms. Inspired by this, in this work, we propose the use of HMMs to identify transient RFI events. We train HMMs to distinguish between the sources of several different types of RFI in a previously recorded dataset. We demonstrate that basic HMMs can be used to classify different RFI events according to their sources in the time-domain, providing useful levels of accuracy.

Original languageEnglish
Title of host publicationProceedings of 2016 Radio Frequency Interference
Subtitle of host publicationCoexisting with Radio Frequency Interference, RFI 2016
PublisherIEEE Press
Number of pages5
ISBN (Electronic)9781509062010
Publication statusPublished - 17 Oct 2016
Externally publishedYes
Event2016 Radio Frequency Interference, RFI 2016 - Socorro, United States of America
Duration: 17 Oct 201620 Oct 2016

Publication series

NameProceedings of 2016 Radio Frequency Interference: Coexisting with Radio Frequency Interference, RFI 2016


Conference2016 Radio Frequency Interference, RFI 2016
Country/TerritoryUnited States of America
Period17 Oct 201620 Oct 2016


  • Hidden Markov Models
  • Radio Frequency Interference
  • Transient Classification


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