Few-Shot Learning UAV Recognition Methods Based on the Tri-Residual Semantic Network
Author:
Affiliation:
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, Canada
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Young Elite Scientist Sponsorship Program
CAST and the Natural Sciences and Engineering Research Council of Canada through its discovery program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation
Link
http://xplorestaging.ieee.org/ielx7/4234/9882525/09795041.pdf?arnumber=9795041
Reference18 articles.
1. Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques
2. RF Detection and Classification of Unmanned Aerial Vehicles in Environments with Wireless Interference
3. RF-based low-SNR classification of UAVs using convolutional neural networks;ozturk;arXiv 2009 05519,2020
4. AirID: Injecting a Custom RF Fingerprint for Enhanced UAV Identification using Deep Learning
5. Composite Interpolated Fast Fourier Transform With the Hanning Window
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