Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model

• URL: http://arxiv.org/abs/2601.06228v1
• Date: Fri, 09 Jan 2026 10:59:46 GMT
• Title: Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model
• Authors: Zhaoze Wang, Changxu Zhang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Ernst Warsitz, Markus Gardill,
• Abstract summary: We propose a conditional generative framework for synthesizing realistic Frequency-Modulated Continuous-Wave radar Range-Azimuth Maps.<n>Our approach leverages a generative diffusion model to generate radar data for multiple object categories, including pedestrians, cars, and cyclists.
• Score: 9.764772760421792
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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