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 |
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21.09.2020
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | de Oliveira, Marcio L. Lima Bekooij, Marco J. G. |
| Author_xml | – sequence: 1 givenname: Marcio L. Lima surname: de Oliveira fullname: de Oliveira, Marcio L. Lima email: m.l.limadeoliveira@utwente.nl organization: Computer Architecture for Embedded Systems, University of Twente,Enschede,The Netherlands – sequence: 2 givenname: Marco J. G. surname: Bekooij fullname: Bekooij, Marco J. G. email: marco.bekooij@nxp.com organization: NXP Semiconductors, University of Twente,Department of Embedded Software and Signal Processing,Eindhoven,The Netherlands |
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| Snippet | In this paper, we discuss the usage of Generative Adversarial Networks (GANs) and Deep Convolutional Autoen-coders (CAE) for creating synthetic Range-Doppler... |
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| SubjectTerms | 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 |
| Title | Generating Synthetic Short-Range FMCW Range-Doppler Maps Using Generative Adversarial Networks and Deep Convolutional Autoencoders |
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