Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6873
Title: Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network
Authors: Gürbüz, Sevgi Zübeyde
Tekeli, Bürkan
Yüksel, Melda
Karabacak, Cesur
Gürbüz, Ali Cafer
Guldogan, Mehmet Burak
Keywords: human micro-Doppler
feature selection
classification
multistatic radar
radar network
Issue Date: 2013
Publisher: IEEE
Source: 16th International Conference on Information Fusion (FUSION) -- JUL 09-12, 2013 -- Istanbul, TURKEY
Abstract: 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.
URI: https://hdl.handle.net/20.500.11851/6873
ISBN: 978-605-86311-1-3
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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