Passive RF localization and sensing have advanced significantly in recent years, yet major challenges remain in highly lossy, high-permittivity environments. Current systems struggle with accurate localization, joint estimation of passive backscattering, and motion-aware sensing under strong propagation distortions. This project proposes a holistic framework that integrates novel UHF backscatter theoretical models and hardware, Bayesian techniques for passive localization and sensing, and SHF tagless motion detection to enhance estimation robustness. The objective is to explore new models, methods and system configurations that advance the scientific foundations of passive RF sensing beyond current capabilities.