Digital twin validates drone sensing over cellular signals in tough airspace
TiamiTwin: A Digital Twin for Bistatic ISAC Drone Sensing, Validated Against Measurements
Networking and Internet Architecture
Summary
Keeping an eye on drones flying near important places is tricky, especially when using existing cell phone signals that weren’t meant for sensing. The authors created a digital twin named TiamiTwin to simulate how well drones can be detected using these signals, then checked its accuracy against real measurements from a live 5G network. They found that common models underestimate key signal properties and that detecting drones requires focusing on their motion since their echoes are much weaker than the background noise. Still, the drone could be reliably spotted most of the time using this moving signal information.
What this means in practice
- •For infrastructure security teams: Use validated digital twin models to assess drone detection capabilities via cellular networks around critical sites in complex signal environments.
- •For drone operations planners: Plan drone flights that maximize detection likelihood by understanding delay and Doppler characteristics from cellular sensing in urban and obstructed areas.
Authors
Mehdi Zafari, Saeede Enayati, A. Lee Swindlehurst, Amitav Mukherjee
Abstract
Monitoring lower airspace over critical infrastructure using cellular signals of opportunity is highly practical because transmitters are pre-deployed, licensed, and continuously active. Digital twins can evaluate the feasibility of such integrated sensing and communication (ISAC) architectures, but their predictive accuracy must be validated against real-world data. This paper reports validation results for TiamiTwin, a digital twin developed for bistatic ISAC drone sensing, using empirical measurements from an operational 5G deployment featuring a commercial band n41 gNB and a receiver separated by 572.8 m over a non-line-of-sight (NLOS) channel. TiamiTwin incorporates three parallel channel representations evaluated on a 240-subcarrier grid: the 3GPP TR 38.901 (Release 19) bistatic ISAC model, a ray-traced site model, and the captured field measurements. Empirical results demonstrate that both statistical and ray-tracing models under-predict the measured root-mean-square (RMS) delay spread by approximately a factor of three. Furthermore, target reflections sit 68 dB below static clutter in power, making target detection entirely dependent on Doppler separation to isolate the drone from zero-Doppler background returns. Despite this severe clutter environment, the target remains separable along 88% of the flight path in the delay, Doppler, or joint delay-Doppler domains.