Metacognition for radar coexistence

Anthony F. Martone, Kelly D. Sherbondy, Jacob A. Kovarskiy, Benjamin H. Kirk, Charles E. Thornton, Jonathan W. Owen, Brandon Ravenscroft, Austin Egbert, Adam Goad, Angelique Dockendorf, R. Michael Buehrer, Ram M. Narayanan, Shannon D. Blunt, Charles Baylis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations


In this paper we investigate a metacognitive radar (MCR) model that comprehensively combines disparate cognitive radar (CR) strategies for advanced performance in congested electromagnetic environments (EME). This model changes CR strategies as the spectral environment and target evolve for efficient radar dynamic spectrum access (DSA). The model first implements spectrum sensing followed by spectrum classification to identify known EME scenarios. These spectral scenarios assess the congestion and complexity of time-frequency data collected by the passive sensing process. This evaluation prioritizes possible CR strategies that are effective for the given spectral conditions. The MCR model then evaluates different CR strategies via learning and selects the technique that provides the best radar performance.

Original languageEnglish (US)
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728168128
StatePublished - Apr 2020
Event2020 IEEE International Radar Conference, RADAR 2020 - Washington, United States
Duration: Apr 28 2020Apr 30 2020

Publication series

Name2020 IEEE International Radar Conference, RADAR 2020


Conference2020 IEEE International Radar Conference, RADAR 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation


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