A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation
Wachtel D, Queiroz S, Schön T, Huber W, Faria L (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 3964-3969
Conference Proceedings Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Event location: Bilbao, ESP
ISBN: 9798350399462
DOI: 10.1109/ITSC57777.2023.10422067
Abstract
Radar target simulations are vital to automotive research as they make it possible to reproduce real scenarios with reasonable accuracy. However, this process comes with high computational costs and limitations. The generated targets are ideal and thus far from reality. Recent studies have led to an alternative to generated synthetic radar data: Generative Adversarial Networks (GAN), a neural network architecture designed to generate realistic data to synthesize images, texts, and more, which is being studied for radar data applications. Until now, only a few studies have explored the approach, especially when considering automotive radars, but showing promising results in its applications. This work proposes a Conditional GAN (CGAN) that synthesizes FMCW radar Range-Doppler targets with Micro-Doppler given a selected object input. The results show that the generated samples are realistic enough to be classified with an accuracy of 82% in a pre-trained classifier, proving that the synthetic data seems to be similar to the real ones but not representing ideal results, as simulation targets do, thus fulfilling an important gap of knowledge for simulation purposes.
Involved external institutions
Germany (DE)
Brazil (BR)How to cite
APA:
Wachtel, D., Queiroz, S., Schön, T., Huber, W., & Faria, L. (2023). A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 3964-3969). Bilbao, ESP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Wachtel, Diogo, et al. "A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation." Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023, Bilbao, ESP Institute of Electrical and Electronics Engineers Inc., 2023. 3964-3969.
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