Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3903
Title: DNN Transfer Learning From Diversified Micro-Doppler for Motion Classification
Authors: Seyfioğlu, Mehmet Saygın
Erol, Barış
Gürbüz, Sevgi Zübeyde
Amin, Moeness G.
Keywords: Synthetic aperture radar
Training
Mathematical model
Radar imaging
Radar cross-sections
Convolutional neural networks (CNNs)
deep neural networks (DNNs)
micro-Doppler simulation
radar classification
residual learning
transfer learning
Issue Date: Dec-2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Seyfioglu, M. S., Erol, B., Gurbuz, S. Z., and Amin, M. G. (2018). DNN transfer learning from diversified micro-Doppler for motion classification. IEEE Transactions on Aerospace and Electronic Systems, 55(5), 2164-2180.
Abstract: Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency signals, such as synthetic aperture radar imagery or micro-Doppler signatures. However, a fundamental challenge is the typically small amount of data available due to the high costs and resources required for measurements. Small datasets limit the depth of DNNs implementable, and limit performance. In this work, a novel method for generating diversified radar micro-Doppler signatures using Kinect-based motion capture simulations is proposed as a training database for transfer learning with DNNs. In particular, it is shown that together with residual learning, the proposed DivNet approach allows for the construction of DNNs and offers improved performance in comparison to transfer learning from optical imagery. Furthermore, it is shown that initializing the network using diversified synthetic micro-Doppler signatures enables not only robust performance for previously unseen target profiles, but also class generalization. Results are presented for 7-class and 11-class human activity recognition scenarios using a 4-GHz continuous wave software-defined radar.
URI: https://hdl.handle.net/20.500.11851/3903
https://ieeexplore.ieee.org/document/8572732
ISSN: 1557-9603
0018-9251
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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