Generating Synthetic Short-Range FMCW Range-Doppler Maps Using Generative Adversarial Networks and Deep Convolutional Autoencoders

In this paper, we discuss the usage of Generative Adversarial Networks (GANs) and Deep Convolutional Autoen-coders (CAE) for creating synthetic Range-Doppler (RD) maps of Frequency-Modulated Continuous-Wave (FMCW) radars for a short-range situation with moving objects, based on measured RD maps of p...

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Veröffentlicht in:	Proceedings of the IEEE National Radar Conference (1996) S. 1 - 6
Hauptverfasser:	de Oliveira, Marcio L. Lima, Bekooij, Marco J. G.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 21.09.2020
Schlagworte:	Autoencoder Convolution Convolutional Deep Learning Doppler-Range FMCW Generative Adversarial Network Generative adversarial networks Generators Neural Network Noise measurement Radar Radar imaging Synthetic Data Training
ISSN:	2375-5318
Online-Zugang:	Volltext
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