dorsal/arxiv
View SchemaSynthetic 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.06228vv1 |
| URL | https://arxiv.org/abs/2601.06228 |
| 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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"date_created": "2026-02-17T05:53:07.512000Z",
"date_modified": "2026-02-17T05:53:07.512000Z",
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"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.",
"arxiv_id": "2601.06228",
"authors": [
"Zhaoze Wang",
"Changxu Zhang",
"Tai Fei",
"Christopher Grimm",
"Yi Jin",
"Claas Tebruegge",
"Ernst Warsitz",
"Markus Gardill"
],
"categories": [
"cs.CV",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"title": "Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model",
"url": "https://arxiv.org/abs/2601.06228",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "9ccc41b9-9185-463a-becc-7fd687266338",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}