Virginia Tech

Networks and Systems Security Lab


High-precision Localization

Accurate localization is foundational to the autonomy, safety, and reliability of modern cyber-physical systems—especially in GPS-denied environments such as indoor spaces, urban canyons, and critical infrastructure zones. From drones and autonomous vehicles to asset tracking and smart manufacturing, the need for robust, high-precision, three-dimensional localization systems is more pressing than ever.

Our research group focuses on next-generation localization frameworks that push the boundaries of accuracy, reliability, and adaptability in challenging environments. We design and evaluate novel positioning systems that operate without GPS, leveraging technologies such as ultrasonic acoustic ranging, Wi-Fi and 5G multi-RAT integration, reconfigurable intelligent surfaces (RIS), and optimized beacon placement. A core theme of our work is mitigating the twin challenges of multipath interference and geometric-induced error—two primary sources of inaccuracy in indoor localization.

We develop algorithms and systems that not only achieve centimeter-level or sub-meter accuracy but also operate robustly in real-world conditions, including areas with poor signal quality or structural interference. Our work systematically explores how the geometry and placement of anchors and beacons influence localization performance and develops optimization algorithms to reduce error, especially in the Z-axis, which is often the most challenging.

Beyond achieving high-accuracy estimates, our research also addresses scalability, real-time operation, and integration with communication infrastructure, making our solutions viable for 5G-enabled drone navigation, autonomous robotics, and intelligent transportation systems. Our contributions have advanced the state of the art in sensor fusion, anchor optimization, and indoor localization architecture, influencing both academic approaches and industrial deployment strategies.

Through rigorous experimentation, simulation, and real-world deployment, we contribute to a deeper understanding of the trade-offs and capabilities of GPS-free localization systems, helping pave the way toward truly autonomous systems that can navigate seamlessly across environments.


Related Publications

PILOT: High-Precision Indoor Localization for Autonomous Drones

Authors: Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park

Published in: IEEE Transactions on Vehicular Technology 2022


OFDRA: Optimal Femtocell Deployment for Accurate Indoor Positioning of RIS-Mounted AVs

Authors: Alireza Famili, Tolga O. Atalay, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park

Published in: IEEE Journal on Selected Areas in Communications (JSAC) 2023


iDROP: Robust localization for indoor navigation of drones with optimized beacon placement

Authors: Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park

Published in: IEEE Internet of Things Journal 2023


Leveraging Isochrons of Nonlinear Oscillators for High-Precision Localization

Authors: Alireza Famili, Georgia Himona, Yannis Kominis, Angelos Stavrou, Vassilios Kovanis

Published in: IEEE Internet of Things Journal 2024


Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems

Authors: Alireza Famili, Shihua Sun, Tolga O. Atalay, Angelos Stavrou

Published in: IEEE Open Journal of the Communications Society 2025


Precision tracking in geofencing systems using deep reinforcement learning

Authors: A Famili, S Sun, T Atalay, A Stavrou

Published in: 2024 IEEE International Performance, Computing, and Communications Conference