Tracking the Occluded Indoor Target with Scattered Millimeter Wave Signal

In our work, we propose an innovative system to accurately infer and track occluded target locations using mmWave beat frequency signals. Our approach combines a classic direction-finding method with advanced deep learning techniques, specifically a convolutional neural network (CNN), to enhance detection capabilities. The dataset includes raw beat frequency signal data from the TI IWR6843ISK rev B with TI mmWAVEICBOOST and the TI DCA1000EVM capture board. Corresponding ground truth data (target position) from the Realsense L515 RGB-D camera is also provided. Additionally, we include middle-processed data, post-processed data for training the CNN, and comprehensive scripts for processing, CNN training, CNN testing, and data visualization. This complete package ensures a robust system for improved accuracy in detecting and tracking targets, even in occluded scenarios.