Fusion of Cellular ISAC and Passive RF Sensing for UAV Detection and Tracking

TL;DR

This paper introduces a UAV detection and tracking system that combines passive RF sensors and radar in an ISAC framework, demonstrating improved accuracy and coverage through real-world experiments and sensor fusion techniques.

Contribution

It presents a novel multi-modal UAV tracking system that fuses RF and radar measurements using a Kalman filter, enhancing robustness and accuracy in outdoor environments.

Findings

Fusion of RF and radar improves tracking accuracy.

Sensor fusion increases coverage without performance loss.

Real-world experiments validate the system's effectiveness.

Abstract

The rapid growth of unmanned aerial vehicles (UAVs) in civilian and critical-infrastructure airspace has created a need for reliable detection and tracking systems that operate under diverse environmental and sensing conditions. This paper presents a UAV detection and tracking system that fuses measurements from a network of passive Keysight N6841A RF sensors and a Ku-band Fortem TrueView R20 radar operating in the FR3 spectrum (16.3 GHz) as an ISAC proxy. Real-world experiments at the NSF AERPAW testbed demonstrate that radar and RF sensing provide complementary strengths under varying geometric, range, and line-of-sight conditions. A Kalman filter using a constant-velocity motion model integrates the asynchronous 2D RF and 3D radar observations, suppressing large standalone errors, improving accuracy over individual modalities, and increasing tracking coverage without degrading performance. These results demonstrate the effectiveness of multi-modal, ISAC-oriented sensing for robust UAV tracking in outdoor environments.