Researchers from Queen Mary University of London, in collaboration with Xi'an Jiaotong University, Pandit Deendayal Energy University, and Chicago State University, have introduced the Joint DAS and GNSS (JDG) system, a hybrid positioning architecture capable of reliable tracking in environments where global navigation satellite systems are unreliable. The findings were presented at the IEEE International Conference on Communications in Glasgow. JDG integrates conventional GPS data with Distributed Acoustic Sensing, a technique that utilizes existing fiber-optic cables as distributed vibration sensors. Buried beneath urban infrastructure, these cables detect minute mechanical shifts caused by human or vehicle movement, converting them into actionable signal data. A deep-learning model fuses the vibration inputs with satellite coordinates, enabling continuous location estimation even when GPS signals are blocked, noisy, or entirely unavailable. Validation occurred during a field trial in southern England, where volunteers traversed a designated route monitored by a roadside fiber-optic line. The JDG model successfully predicted participant locations based on combined data streams, consistently outperforming GPS-only tracking and other predictive algorithms. The system demonstrated resilience during complete GPS outages and maintained accuracy on lower-powered devices with limited data sampling rates, suggesting compatibility with a broad range of smartphones and IoT endpoints. The technology targets persistent location challenges in dense urban canyons, indoor facilities, and subterranean spaces. Developers anticipate JDG will enhance smart transportation networks, emergency response operations, and autonomous navigation, providing a robust alternative for positioning where satellite dependencies create functional gaps.