Analysis of machine learning methods for clutter classification

Richard L. Washington, Dmitriy S. Garmatyuk, Saba Mudaliar, Ram M. Narayanan

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


There are various scenarios, whether they be commercial or defense, where privacy is important. In communications, the metrics of low probability of interception is often used to measure the signal's ability to resist interception and decoding by unauthorized parties. Joint radar sensing and communications (RadarCom) has been of interest recently and an important requirement of RadarCom signals is its immunity to interceptions. In this context it is of interest to understand the statistics of background clutter. This paper uses machine learning (ML) approaches to classify and model clutter in presence of noise/interference. We employ 32 sub-carrier orthogonal frequency division multiplexing waveforms as a basis for clutter return collection and subsequent use as RadarCom signals. We then present the ML combination method with the best classification accuracy of 78.9%.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXV
EditorsKenneth I. Ranney, Ann M. Raynal
ISBN (Electronic)9781510643215
StatePublished - 2021
EventRadar Sensor Technology XXV 2021 - Virtual, Online, United States
Duration: Apr 12 2021Apr 16 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceRadar Sensor Technology XXV 2021
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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