A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking

• URL: http://arxiv.org/abs/2603.00106v1
• Date: Wed, 18 Feb 2026 08:09:17 GMT
• Title: A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking
• Abstract summary: We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2.<n>All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings.<n>The dataset contains RF recordings from 26 UAV units spanning 8 distinct models.
• Score: 53.65328703290737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2, which is collected using a dedicated acquisition platform under diverse operational conditions. All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings. The dataset is systematically organized into seven well-defined subsets corresponding to different operational and signal composition scenarios to facilitate structured experimentation. Each file follows a clearly annotated naming convention to enable convenient data indexing and reproducible analysis. The dataset contains RF recordings from 26 UAV units spanning 8 distinct models, captured across varying flight states, altitudes, speeds, acquisition days, and receiver configurations. By covering diverse acquisition settings and signal compositions, the dataset provides a comprehensive resource for future UAV RF signal research, including RF fingerprinting (RFF) identification, model-level recognition, flight state analysis, time-varying RFF study, and interference-aware signal processing.

Related papers

• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method [80.50361258830928]
  UAVNet-MS is the first multispectral dataset for fine-grained small-UAV detection.<n>We propose MFDNet, a dual-stream baseline addressing array-induced parallax and spatial-spectral fusion.<n>This work provides foundational dataset, strong baseline, and benchmark for multispectral UAV monitoring research.
  arXiv  Detail & Related papers  (2026-05-20T09:49:04Z)
• Semantically Annotated Multimodal Dataset for RF Interpretation and Prediction [4.872063946977515]
  Current limitations in wireless modeling and radio frequency (RF)-based AI are driven by a lack of high-quality, measurement-based datasets that connect RF signals to their physical environments.<n>We propose a new class of multimodal datasets that combines RF measurements with auxiliary modalities like high-resolution cameras and lidar to bridge the gap between RF signals and their physical causes.
  arXiv  Detail & Related papers  (2026-04-01T22:18:01Z)
• ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals [8.411477071838592]
  We propose a novel foundation model ECHO that integrates an advanced band-split architecture with frequency positional embeddings.<n>We evaluate our method on various kinds of machine signal datasets.
  arXiv  Detail & Related papers  (2025-08-20T13:10:44Z)
• RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification [6.838837917253087]
  RFUAV is a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification.<n>RFUAV comprises approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs.
  arXiv  Detail & Related papers  (2025-03-12T03:46:09Z)
• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods.<n>A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Joint Signal Detection and Automatic Modulation Classification via Deep Learning [34.580846369146435]
  This paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario. We present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment. Our code and dataset are now available as open-source.
  arXiv  Detail & Related papers  (2024-04-29T15:40:19Z)
• Multi-task Learning for Radar Signal Characterisation [48.265859815346985]
  This paper presents an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.
  arXiv  Detail & Related papers  (2023-06-19T12:01:28Z)
• Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case [10.698553177585973]
  We present and release a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions.
  arXiv  Detail & Related papers  (2022-01-06T19:07:57Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Conditioning Trick for Training Stable GANs [70.15099665710336]
  We propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. We force the generator to get closer to the departure from normality function of real samples computed in the spectral domain of Schur decomposition.
  arXiv  Detail & Related papers  (2020-10-12T16:50:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.