Yan Li

JHUAPL
“Active and Passive Cellular Multi-Static ISAC for White-Space-Aware Drone Tracking”

Session Materials: Link

Speaker Bio: 

Dr. Yan Li is a Senior Staff Engineer and Principal Investigator at the Johns Hopkins University Applied Physics Laboratory, specializing in the convergence of RF sensing, communications, and AI/ML. With over 15 years of experience, he leads R&D efforts in distributed passive tracking and spectral situational awareness. Dr. Li has published multiple paper on the use of deep learning for RF channel adaptive waveforms and has a distinguished publication record in venues such as MILCOM, INFOCOM, and SenSys. He holds a PhD in Computer Science and multiple graduate degrees from the University of Maryland and Johns Hopkins University. His current work focuses on leveraging digital twins and machine learning to enhance UAS detection and secure communication links in contested environments.

Abstract: 

Dense 5G cellular deployments offer a promising infrastructure for Integrated Sensing and Communications (ISAC), particularly for countering small unmanned aerial systems (CsUAS). However, unlike dedicated radar, cellular-based sensing must contend with fixed reconfigurable antenna patterns, limited transmit power, and traffic-dependent resource availability. This paper proposes a multi-static ISAC architecture for detecting and tracking low-altitude drones using cooperative gNodeBs (gNBs) operating at 3 GHz. Our contribution is three-fold: (1) a whitespace detection mechanism that opportunistically embeds sensing waveforms into the 5G time-frequency grid without degrading Quality-of-Service (QoS); (2) a tracking-aware optimization framework for joint waveform and beamforming design; and (3) a hybrid multi-sensor tracking algorithm that combines particle filtering for hypothesis management with conditional Kalman filtering for kinematic estimation. To validate our approach, we developed a high-fidelity digital twin using OpenStreetMap data, realistic gNB placements, and empirical drone radar cross-section (RCS) models. Results indicate that our framework significantly enhances tracking continuity and accuracy over passive-only baselines while maintaining nearly all baseline communication capacity.