One-shot generative distribution matching for augmented RF-based UAV identification

This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based id...

Full description

Saved in:
Bibliographic Details
Published in:Machine learning with applications Vol. 20; p. 100638
Main Authors: Kazemi, Amir, Basiri, Salar, Kindratenko, Volodymyr, Salapaka, Srinivasa
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2025
Elsevier
Subjects:
Data Augmentation
Extrapolation
Low-data regimes
Synthetic data
Wasserstein metric
Extrapolation
Data Augmentation
Low-data regimes
Wasserstein metric
Synthetic data
ISSN:2666-8270, 2666-8270
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based identification methods ineffective. To address these complications, the study introduces the rigorous use of one-shot generative methods for augmenting transformed RF signals, offering a significant improvement in UAV identification. This approach, when utilizing a distributional distance metric, demonstrates significant promise in low-data regimes, outperforming deep generative methods such as conditional generative adversarial networks (GANs) and variational autoencoders (VAEs). The paper provides a theoretical guarantee for the effectiveness of one-shot generative models in augmenting limited data, setting a precedent for their application in limited RF environments. This research also contributes to learning techniques in low-data regime scenarios, which may include complex sequences beyond images and videos. The code and links to datasets used in this study are available at https://github.com/amir-kazemi/uav-rf-id. •We derive an upper bound on the distributional distance between synthetic and real data.•One-shot generative algorithms can outperform deep models in low-data classification.•We demonstrate the approach’s efficacy for UAV identification in RF-limited settings.
ISSN:2666-8270
2666-8270
DOI:10.1016/j.mlwa.2025.100638