GMM-based target classification for ground surveillance Doppler radar

Research output: Contribution to journalArticlepeer-review

145 Scopus citations


An automatic target recognition (ATR) algorithm, based on greedy learning of Gaussian mixture model (GMM) is developed. 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
Pages (from-to)267-278
Number of pages12
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number1
StatePublished - 1 Jan 2006

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering


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