Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network

Gurbuz S. Z., Tekeli B., YÜKSEL TURGUT A. M., Karabacak C., Gurbuz A. C., Guldogan M. B.

16th International Conference on Information Fusion (FUSION), İstanbul, Türkiye, 9 - 12 Temmuz 2013, ss.610-616 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.610-616
  • Anahtar Kelimeler: human micro-Doppler, feature selection, classification, multistatic radar, radar network, SIGNATURES
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. Although dozens of features have been proposed for these purposes, little work has examined the issue of which features are more important - i.e., have a greater impact on classification performance - than others. In this work, an information theoretic approach is applied to compute the importance ranking of features prior to classification for the specific problem of discriminating human walking from running. Results show that the ranking of features according to mutual information directly relates to classification performance using support vector machines.