用3D建模生成毫米波信号，解决数据稀缺难题。
One Snapshot is All You Need: A Generalized Method for mmWave Signal Generation
- 基于3D网格构建物理传播模型，合成人体与环境反射信号
- 在三种环境下，距离-角度和微多普勒相似度分别超0.91和0.89
- 适用于隐私敏感场景的仿真数据生成，适合传感系统研发
毫米波无线感知系统相较于传统视觉方法具有隐私保护和低光照下表现优异等优势，广泛应用于定位、手势识别等场景。然而，面向多样化应用的完整毫米波数据集仍十分稀缺，主要受限于预处理特征（如点云或雷达热图）及标注格式不统一。为此，我们提出mmGen——一种针对全场景毫米波信号生成的通用框架。通过构建物理信号传播模型，mmGen从3D网格中合成人体与环境反射的毫米波信号，并引入材料属性、天线增益及多路径反射的建模方法，提升信号真实性。我们使用商用毫米波设备与Kinect传感器搭建原型系统进行实验，结果表明，在三个不同环境中，合成信号与真实采集信号在距离-角度和微多普勒特征上的平均相似度分别超过0.91和0.89，验证了mmGen的有效性与实用性。
原文摘要 · Abstract (English)
Wireless sensing systems, particularly those using mmWave technology, offer distinct advantages over traditional vision-based approaches, such as enhanced privacy and effectiveness in poor lighting conditions. These systems, leveraging FMCW signals, have shown success in human-centric applications like localization, gesture recognition, and so on. However, comprehensive mmWave datasets for diverse applications are scarce, often constrained by pre-processed signatures (e.g., point clouds or RA heatmaps) and inconsistent annotation formats. To overcome these limitations, we propose mmGen, a novel and generalized framework tailored for full-scene mmWave signal generation. By constructing physical signal transmission models, mmGen synthesizes human-reflected and environment-reflected mmWave signals from the constructed 3D meshes. Additionally, we incorporate methods to account for material properties, antenna gains, and multipath reflections, enhancing the realism of the synthesized signals. We conduct extensive experiments using a prototype system with commercial mmWave devices and Kinect sensors. The results show that the average similarity of Range-Angle and micro-Doppler signatures between the synthesized and real-captured signals across three different environments exceeds 0.91 and 0.89, respectively, demonstrating the effectiveness and practical applicability of mmGen.
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