# Generative Adversarial Network for Radar Signal Generation

arXiv: https://arxiv.org/abs/2008.03346 · HTML: https://mlsanity.com/paper/2008.03346/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection](https://mlsanity.com/paper/2112.03478/) (2021)
- [Generative Adversarial Networks for Labelled Vibration Data Generation](https://mlsanity.com/paper/2112.08195/) (2021)
- [Generative Adversarial Networks for Data Generation in Structural Health Monitoring](https://mlsanity.com/paper/2112.08196/) (2021)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [Radar2Shape: 3D Shape Reconstruction from High-Frequency Radar using Multiresolution Signed Distance Functions](https://mlsanity.com/paper/2511.17484/) (2025)

## Abstract (arXiv)

A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. As such, this paper proposes the design of a GAN for application in radar signal generation. Data collected using the Finite-Difference Time-Domain (FDTD) method on three concealed object classes (no object, large object, and small object) were used as training data to train a GAN to generate radar signal samples for each class. The proposed GAN generated radar signal data which was indistinguishable from the training data by qualitative human observers.