仅用一个样本实现无相位射频成像，突破设备精度瓶颈。
RINN: One Sample Radio Frequency Imaging based on Physics Informed Neural Network
- 用物理约束替代真实值对比，适配无处不在的射频信号
- 仅需单一样本、无相位数据，仍达0.11的RRMSE指标
- 适合低资源、非视距场景下的智能感知应用
由于能够在非视距和低光照环境下工作，射频（RF）成像技术有望为具身智能与多模态感知带来新可能。然而，广泛使用的射频设备（如Wi-Fi）常难以提供高精度电磁测量和大规模数据集，制约了该技术的应用。本文结合物理信息神经网络（PINN）思想，设计了RINN网络，利用物理约束替代真实值对比约束，并适配通用射频信号特性，使RINN能在仅有一个样本、无相位且含幅度噪声的情况下完成射频成像。数值评估结果显示，相较于基于相位数据的5种经典算法，RINN在无相位条件下成像效果良好，关键指标如RRMSE（0.11）表现相当。RINN为射频成像技术的普适化发展提供了新路径。
原文摘要 · Abstract (English)
Due to its ability to work in non-line-of-sight and low-light environments, radio frequency (RF) imaging technology is expected to bring new possibilities for embodied intelligence and multimodal sensing. However, widely used RF devices (such as Wi-Fi) often struggle to provide high-precision electromagnetic measurements and large-scale datasets, hindering the application of RF imaging technology. In this paper, we combine the ideas of PINN to design the RINN network, using physical constraints instead of true value comparison constraints and adapting it with the characteristics of ubiquitous RF signals, allowing the RINN network to achieve RF imaging using only one sample without phase and with amplitude noise. Our numerical evaluation results show that compared with 5 classic algorithms based on phase data for imaging results, RINN's imaging results based on phaseless data are good, with indicators such as RRMSE (0.11) performing similarly well. RINN provides new possibilities for the universal development of radio frequency imaging technology.
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