被动射频无人机链路检测中的仅控制器误确认

中文摘要 AI 辅助

被动射频（RF）感知被广泛用于反无人机（counter-UAV）检测。现有研究通常报告在背景RF或WiFi/蓝牙干扰下的高准确率，但很少隔离没有关联飞行器的仅控制器操作。我们针对三种商用无人机平台（DJI Phantom 3 4K、Hubsan H501S、DJI Mavic Mini）开展了一项双频段软件定义无线电（SDR）测量研究，涵盖环境、仅控制器和关联状态。两台USRP B210接收机在二十轮中同时扫描八个2.4 GHz和十二个5.8 GHz观测窗口。我们首先仅使用关联和环境扫描训练一个基于能量的检测器。在四个排序的并发驻留步骤（8秒）中，它实现了0.992的关联-对环境平衡准确率，但在60次仅控制器扫描中误确认了30次（FCRctrl = 0.500）。然后，我们将仅控制器扫描纳入训练，并在显式仅控制器误确认率（FCR）约束下使用确认、拒绝和延迟输出。对于合并的全平台分析，该约束将观测到的FCRctrl从0.350降至0.050，而确认关联的TPR从0.900降至0.400，42.1%的扫描被延迟。相应的紧凑扫描对Hubsan和Mavic的表现明显优于Phantom。硬件在环实验将测量的墙钟时间从82.9秒减少到28.9秒。这些测量表明，关联-对背景准确率不能衡量仅控制器误确认，且该错误应单独报告。

英文摘要

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.

发表机构

• Mason Innovation Labs, George Mason University（乔治梅森大学）

机构由 AI 辅助整理，请以论文原文为准。