Micro-Doppler based target classification using multi-feature integration

A W Miller, C Clemente, A Robinson, D Greig, A M. Kinghorn, J J. Soraghan

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

14 Citations (Scopus)


Three novel micro-Doppler feature extraction algorithms are presented and applied to a dataset containing real X-band radar data of moving ground targets. In each case data dimensional reduction was carried out using principal component analysis (PCA) and incorporated into the feature extraction process. Extracted features are classified using a support vector machine (SVM) classifier. It was found that all three algorithms were able to produce classification accuracies in excess of 90%. The performance of the different algorithms are shown to depend on the method used and the degree of dimensionality reduction imposed at the PCA stage.
Original languageEnglish
Title of host publicationIET Intelligent Signal Processing Conference 2013 (ISP 2013)
Number of pages6
Publication statusPublished - 2013
EventIET Intelligent Signal Processing Conference - London, United Kingdom
Duration: 2 Dec 20133 Dec 2013


ConferenceIET Intelligent Signal Processing Conference
Country/TerritoryUnited Kingdom


  • micro doppler
  • target classification
  • robust principal component analysis
  • minimum covariance determinant
  • support vector machine classification


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