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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Bibliographic Details
Published in:Proceedings of the IEEE National Radar Conference (1996) pp. 1 - 6
Main Authors: de Oliveira, Marcio L. Lima, Bekooij, Marco J. G.
Format: Conference Proceeding
Language:English
Published: IEEE 21.09.2020
Subjects:
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 Access:Get full text
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Summary: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 pedestrians and cyclists. Instead of using regular mathematical functions or heavy radar simulations, we have used an Artificial Neural Network (ANN) model to generate new data. By using our synthetic data, we can automatically have ground-truth data without the need for manual labor; easily create large synthetic datasets; hardly use much computational power after training. To evaluate our method, we have trained a detector system with just synthetic data, and it was capable of detecting moving objects correctly, on actual Range-Doppler maps, 11.6% better than when using a small dataset.
ISSN:2375-5318
DOI:10.1109/RadarConf2043947.2020.9266348