Unsupervised Learning Using Generative Adversarial Networks on Micro-Doppler Spectrograms

Garcia Doherty, H., Cifola, L., Harmanny, R. and Fioranelli, F. (2019) Unsupervised Learning Using Generative Adversarial Networks on Micro-Doppler Spectrograms. In: 16th European Radar Conference (EuRAD 2019), Paris, France, 2-4 Oct 2019, ISBN 9782874870576

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Abstract

This paper presents the implementation of a Generative Adversarial Network (GAN) and Adversarial Autoencoder (AAE) trained in an unsupervised manner using micro-Doppler (mD) spectrograms of human gait. Once the GAN network was trained, the domain where micro-Doppler feature learning happens is inspected. This domain is then accessed by building the AAE and different network visualizations are shown. The benefits of unsupervised training are highlighted by investigating the self-learned spectrogram features, revealing the potential of unsupervised adversarial training techniques for mD spectrogram feature learning methods.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco
Authors: Garcia Doherty, H., Cifola, L., Harmanny, R., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9782874870576
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