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.

We demonstrate a non-invasive Wi-Fi sensing system that uses channel state information (CSI) data and deep neural network (DNN) models to reconstruct the cross-section images of potato tubers underground. We design a Wi-Fi mesh network that can leverage both the space and frequency diversities of the wireless network. We apply a multi-branch convolutional neural network (CNN) model to perform data-driven image reconstruction. We have performed extensive experiments to build a Wi-Fi potato sensing dataset, and our demo and experimental evaluations show that the Wi-Fi system outperforms the state-of-the-art root tuber wireless sensing system in terms of image quality and estimation accuracy.

Radio frequency sensors can penetrate non-metal objects and provide complementary information to vision sensors for person identification (PID) purposes. However, there is a lack of research on millimeter wave (mmWave) radar for PID under occlusions, particularly in addressing the open-set recognition problem. Thus, we propose an open-set occluded PID (OSO-PID) framework that can deal with various obstacle and occlusion scenarios with open-set recognition capability. We first introduce a new dataset, mmWave-ocPID, comprising mmWave radar measurements and RGB-depth images, collected from 23 human subjects. We next design a novel neural network, mm-PIDNet, for occluded person identification using mmWave radar measurements. mm-PIDNet incorporates a transformer encoder, a bidirectional long short-term memory module, and a novel supervised contrastive learning module to improve PID performance. For open-set recognition, we enhance the mmWave radar-based PID method by integrating supervised contrastive learning with the Weibull models, which can identify out-of-distribution samples. We perform extensive indoor experiments with a variety of obstacles and occlusion scenarios. Our experimental results show that mm-PIDNet achieves an F1-score of 0.93 on average, outperforming state-of-the-art methods by up to 13.41% for occluded cases. For open-set PID, the OSO-PID framework achieves an F1-score above 0.8 when the openness is less than 14.36%.

We propose mmWave-ocPID, a person identification (PID) method with millimeter-wave radar to identify individuals even when they are heavily occluded by obstacles. We collect a multi-modal dataset comprising mmWave radar point clouds and RGB images obtained from 9 human subjects, with over 180,000 frames for each modality. The mmWave-ocPID prototype employs a novel Neural Network integrated with two augmentation strategies for learning. Our initial experimental results show that mmWave-ocPID can achieve high identification accuracy, even when most of the human body of an individual is occluded in a controlled environment.