Target classification using Gaussian mixture model for ground surveillance Doppler radar

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


An automatic target recognition (ATR) algorithm, based on greedy learning of Gaussian mixture model (GMM) is developed in this work. The GMMs were obtained for a wide range of ground surveillance radar targets such as: walking person(s), tracked or wheeled vehicles, animals and clutter. Maximum-likelihood (ML) and "majority voting" decision schemes were applied to these models for target classification. The corresponding classifiers were trained and tested using distinct databases of target echoes, recorded by ground surveillance radar. ML and "majority voting" classifiers obtained classification rates of 88% and 96%, correspondingly. Both classifiers outperform trained human operators.

Original languageEnglish
Article number1435957
Pages (from-to)910-915
Number of pages6
JournalIEEE National Radar Conference - Proceedings
Issue numberJanuary
StatePublished - 1 Jan 2005
Event2005 IEEE International Radar Conference Record, RADAR 2005 - Arlington, United States
Duration: 9 May 200512 May 2005


  • ATR
  • GMM
  • Ground surveillance radar
  • Majority voting
  • Target classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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