Controller-Only False Confirmation in Passive RF UAV Link Detection
cs.CR
Submitted: 2026-09-21
Updated: 2026-09-21
Code: https://github.com/rajyay/controlleronly-rf-uav
License: http://creativecommons.org/licenses/by/4.0/
The gist: Passive radio frequency (RF) sensing is widely used for counter-unmanned-aerial-vehicle (counter-UAV) detection.
Terminology
Abstract
Passive radio frequency (RF) sensing is widely used for counter-unmanned-aerial-vehicle (counter-UAV) detection. Existing studies commonly report high accuracy against background RF or WiFi/Bluetooth interference, but rarely isolate controller-only operation without a linked aircraft. We present a dual-band software-defined radio (SDR) measurement study with ambient, controller-only, and linked states for three commercial UAV platforms (DJI Phantom 3 4K, Hubsan H501S, DJI Mavic Mini). Two USRP B210 receivers simultaneously scan eight 2.4 GHz and twelve 5.8 GHz observation windows across twenty rounds. We first train an energy-based detector using linked and ambient scans only. At four ranked concurrent dwell steps (8 s), it achieves 0.992 linked-versus-ambient balanced accuracy but false-confirms 30 out of 60 controller-only scans (FCRctrl = 0.500). We then include controller-only scans in training and use confirm, reject, and defer outputs under an explicit controller-only false-confirmation-rate (FCR) constraint. For the pooled all-platform analysis, the constraint reduces observed FCRctrl from 0.350 to 0.050, while confirm-linked TPR decreases from 0.900 to 0.400 and 42.1% of scans are deferred. The corresponding compact scan performs substantially better for Hubsan and Mavic than for Phantom. A hardware-in-loop experiment reduces measured wall-clock time from 82.9 s to 28.9 s. These measurements show that linked-versus-background accuracy does not measure controller-only false confirmation and that this error should be reported separately.
Sources
- RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
- CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception
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