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Classification of Micro-Doppler Signatures Measured by Doppler Radar Through Transfer Learning
- Classification of Micro-Doppler Signatures Measured by Doppler Radar Through Transfer Learning
- Alnujaim, Ibrahim; Oh, Daegun; Park, Ikmo; Kim, Youngwook
- DGIST Authors
- Oh, Daegun
- Issue Date
- 13th European Conference on Antennas and Propagation, EuCAP 2019
- 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.
- Institute of Electrical and Electronics Engineers Inc.
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