Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model
Abstract
The scarcity and low diversity of well-annotated automotive radar datasets often limit the performance of deep-learning-based environmental perception. To overcome these challenges, we propose a conditional generative framework for synthesizing realistic Frequency-Modulated Continuous-Wave radar Range-Azimuth Maps. Our approach leverages a generative diffusion model to generate radar data for multiple object categories, including pedestrians, cars, and cyclists. Specifically, conditioning is achieved via Confidence Maps, where each channel represents a semantic class and encodes Gaussian-distributed annotations at target locations. To address radar-specific characteristics, we incorporate Geometry Aware Conditioning and Temporal Consistency Regularization into the generative process. Experiments on the ROD2021 dataset demonstrate that signal reconstruction quality improves by \SI{3.6}{dB} in Peak Signal-to-Noise Ratio over baseline methods, while training with a combination of real and synthetic datasets improves overall mean Average Precision by 4.15% compared with conventional image-processing-based augmentation. These results indicate that our generative framework not only produces physically plausible and diverse radar spectrum but also substantially improves model generalization in downstream tasks.
Cite
@article{arxiv.2601.06228,
title = {Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model},
author = {Zhaoze Wang and Changxu Zhang and Tai Fei and Christopher Grimm and Yi Jin and Claas Tebruegge and Ernst Warsitz and Markus Gardill},
journal= {arXiv preprint arXiv:2601.06228},
year = {2026}
}