# Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model Source: [Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model]() Collection index: [llms.txt]() Data updated: 2026-01-14T05:00:04.658Z Authors: Zhaoze Wang, Changxu Zhang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Ernst Warsitz, Markus Gardill Published: 2026-01-09T10:59:46.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Radar Object Detection on ROD 2021 (Campus Road)](): Radar Object Detection; ROD 2021 (Campus Road); mAP: 36.95. - [Radar Object Detection on ROD City Street 2021](): Radar Object Detection; ROD City Street 2021; mAP: 21.01. - [Radar Object Detection on ROD Highway 2021](): Radar Object Detection; ROD Highway 2021; mAP: 0.3382. - [Radar Object Detection on ROD 2021](): Radar Object Detection; ROD 2021; mAP: 0.3565. - [Radar Object Detection on ROD Parking Lot 2021](): Radar Object Detection; ROD Parking Lot 2021; mAP: 57.35. ## Original sources and implementation [Original paper]() [PDF]()