Federated motion uncertainty improves uav tracking across multiple operators
Federated Calibration of Motion Uncertainty for UAV Tracking in Multi-Operator ISAC Systems
Information Theory
Summary
Tracking drones using multiple base stations can help improve accuracy, but sharing data directly between different operators is often not possible. The authors present a method that lets each operator keep its data private while still combining tracking information to make better predictions. They use a special technique to handle uncertain drone movements and then merge these estimates without knowing how their errors are related. This combined prediction helps each operator adjust their transmissions, improving tracking accuracy and maintaining communication quality.
What this means in practice
- •For telecommunication operators: Coordinate tracking of drones across operators without sharing raw data for better UAV location prediction and communication performance.
- •For drone fleet managers: Improve drone position tracking accuracy when multiple service providers operate sensing equipment in overlapping areas without exposing sensitive data.
Authors
Qiming Li, Lei Zhang, Muhammad Ali Imran, Lina Mohjazi
Abstract
In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused prediction then guides each operator's transmission design subject to communication QoS, power, and tracking constraints. We further establish exact rank one recovery for the communication covariances and finite interval bounds on local tracking uncertainty. Simulations show improved consistency of tracking uncertainty under substantial motion model mismatch. CI avoids overconfidence under correlated errors, while using the current fused prediction reduces RMSE by about 31% relative to transmission designed for communication alone, with less than 2% additional power. Ablation further shows that the power difference between current and outdated predictions is driven mainly by predictive covariance rather than predictive mean.