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Classification of Micro-Doppler Signatures Measured by Doppler Radar Through Transfer Learning

Title
Classification of Micro-Doppler Signatures Measured by Doppler Radar Through Transfer Learning
Authors
Alnujaim, IbrahimOh, DaegunPark, IkmoKim, Youngwook
DGIST Authors
Oh, Daegun
Issue Date
2019-04-03
Citation
13th European Conference on Antennas and Propagation, EuCAP 2019
Type
Conference
ISBN
9788890701887
Abstract
In this paper, we investigate the feasibility of using transfer learning for the classification of micro-Doppler signatures measured by Doppler radar. A target with a non-grid body generates micro-Doppler signatures when measured by Doppler radar, which serve as an important feature for classification. However, the radar dataset is, in general, insufficient because of the high cost of its measurements. To overcome the problem of data deficiency, we propose transfer learning, which involves borrowing a classifier that has already been trained for other applications. In particular, we borrow a network trained for other micro-Doppler spectrograms rather than optical images. For the construction of the training dataset, we augment said data through generative adversarial networks. This idea is verified using human activity data measured by Doppler radar. © 2019 European Association on Antennas and Propagation.
URI
http://hdl.handle.net/20.500.11750/10274
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
ETC2. Conference Papers


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