RINN: One Sample Radio Frequency Imaging based on Physics Informed Neural Network
This addresses the challenge of high-precision RF imaging for embodied intelligence and multimodal sensing in non-line-of-sight environments, offering a novel approach but with incremental improvements over existing methods.
The paper tackled the problem of radio frequency (RF) imaging with limited data by proposing RINN, a physics-informed neural network that uses physical constraints to enable imaging with only one noisy, phaseless sample, achieving results with RRMSE of 0.11 comparable to classic phase-based methods.
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.