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Title: Data-Dependent Micro-Doppler Feature Selection
Authors: Erol, Barış
Çağlıyan, Bahri
Tekeli, Bürkan
Gürbüz, Sevgi Zübeyde
Keywords: Micro-Doppler signatures
feature selection
human acticity classification
Issue Date: 2015
Publisher: IEEE
Source: 23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: A vast number of features have been proposed over the years for classification of radar micro-Doppler signatures. However, the degree to which a feature may contribute in discriminating between classes depends upon a variety of operational considerations, such as antenna-target aspect angle, signal-to-noise ratio (SNR), and dwell time. Moreover, utilization of all features in every circumstance does not necessarily ensure optimal classification performance. Oftentimes a well-selected subset of robust features yield better results. In this work, the variance of micro-Doppler feature estimates are examined under a variety of operational conditions and used to select feature subsets. The classification performance of data-dependent feature subsets are compared to that attained without any feature selection. Results show that data-dependent feature selection yields higher correct classification rates over a wider range of operational situations.
ISBN: 978-1-4673-7386-9
ISSN: 2165-0608
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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