Data-driven RF Tomography via Cross-modal Sensing and Continual Learning

Data-driven radio frequency (RF) tomography has shown great potential to detect underground targets due to the capability of RF signals to penetrate soil. However, it is still challenging to achieve robust detection performance in dynamic environments. In this work, we design a cross-modal sensing system with RF and visual sensors and propose to train an RF sensing deep neural network (DNN) model following the cross-modal learning approach. We also propose to apply continual learning to automatically update the DNN model in a dynamic environment. Specifically, we design an environmental change detector and a one-shot fine-tuning module and integrate them into the DNN model to reconstruct the cross-section images of underground tubers even with significant changes in RF signals. Experimental results show that our approach achieves an average equivalent diameter error of 2.29 cm, 23.2% improvement upon the state-of-the-art approach. Our code and dataset are both made publicly available.