Abstract
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.
Abstract (translated)
无线传感系统，尤其是采用毫米波（mmWave）技术的系统，在隐私保护和低光照条件下表现优异，相较于传统的视觉方法具有明显的优势。这些系统利用调频连续波（FMCW）信号，在人体定位、手势识别等人机交互应用中表现出色。然而，针对多样应用场景的全面毫米波数据集依然稀缺，受限于预处理签名（如点云或反射幅度热图）及不一致的注释格式。为克服这些限制，我们提出了mmGen这一新颖且通用化的框架，专用于全场景毫米波信号生成。 通过构建物理信号传输模型，mmGen能够从构造出的3D网格中合成人体和环境反射的毫米波信号。此外，我们引入了方法来考虑材料属性、天线增益及多径反射的影响，以增强所生成信号的真实感。在原型系统上进行了广泛的实验，该系统结合了商用毫米波设备与Kinect传感器。结果显示，在三种不同环境中合成信号和实际捕获信号的范围-角度（Range-Angle）和微多普勒签名之间的平均相似度分别超过0.91和0.89，这证明了mmGen的有效性和实用价值。 这项研究为未来的毫米波技术开发奠定了坚实的基础，并展示了其在各种环境条件下的广泛应用潜力。
URL
https://arxiv.org/abs/2503.21122
