A labeled RF signal dataset for UAV detection and classification in the 2.4GHz band under Wifi and Bluetooth coexistence

IF 1.9 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2026-08-01 Epub Date: 2026-08-05 DOI:10.1016/j.dib.2026.113127

Saber Mgannem, Radhoine Aloui, Bilel Hamdi, Sofien Mhatli, Ignacio Llamas-Garro

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引用次数: 0

Abstract

This article presents a labeled radio frequency (RF) dataset for the detection and classification of unmanned aerial vehicles (UAVs) operating in the 2.4 GHz industrial, scientific, and medical (ISM) band. The dataset was acquired using a USRP B210 software-defined radio equipped with bidirectional antennas in an indoor and semi-controlled wireless environment containing realistic WiFi and Bluetooth interference. Data acquisition was performed under multiple operational scenarios, including drone idle, take-off, hovering, movement, and absence of UAV activity. The collected RF signals were transformed into time–frequency spectrogram images using the Short-Time Fourier Transform (STFT) to support machine learning and deep learning applications [1]

The dataset was annotated using LabelImg [2] in a YOLO-compatible format to support object detection and classification tasks. This resource is intended for researchers working on RF-based drone detection, wireless spectrum monitoring [3], and deep learning-based signal classification. The dataset enables benchmarking under realistic interference conditions and supports reproducible research in UAV detection systems.

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Wifi与蓝牙共存下2.4GHz频段无人机检测分类标记射频信号数据集

本文提出了一个标记射频（RF）数据集，用于检测和分类在2.4 GHz工业、科学和医疗（ISM）频段运行的无人机（uav）。数据集是使用USRP B210软件定义无线电获得的，该无线电配备了双向天线，在室内和半受控的无线环境中，包含真实的WiFi和蓝牙干扰。数据采集是在多种操作场景下进行的，包括无人机空闲、起飞、悬停、移动和没有无人机活动。利用短时傅里叶变换（STFT）将采集到的射频信号转换成时频谱图图像，支持机器学习和深度学习应用[1]。数据集使用LabelImg[2]以兼容ylo的格式进行注释，以支持目标检测和分类任务。本资源面向从事基于射频的无人机检测、无线频谱监测[3]和基于深度学习的信号分类的研究人员。该数据集能够在现实干扰条件下进行基准测试，并支持无人机探测系统的可重复性研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.