UAV Classification Utilizing Radar Digital Twins
Author:
Affiliation:
1. University of Waterloo,Department of Electrical and Computer Engineering,Waterloo,Ontario,Canada
Funder
NSERC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10237348/10237371/10237683.pdf?arnumber=10237683
Reference14 articles.
1. Extraction of Global and Local Micro-Doppler Signature Features From FMCW Radar Returns for UAV Detection
2. Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum
3. An extended one-versus-rest support vector machine for multi-label classification
4. Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies
5. Model-Aided Drone Classification Using Convolutional Neural Networks
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2. Frequency-Modulated Continuous-Wave Radar Perspectives on Unmanned Aerial Vehicle Detection and Classification: A Primer for Researchers with Comprehensive Machine Learning Review and Emphasis on Full-Wave Electromagnetic Computer-Aided Design Tools;Drones;2024-08-02
3. Unveiling Aerial Threats: Enhancing UAV Classification Through Radar Digital Twins;2024 International Telecommunications Conference (ITC-Egypt);2024-07-22
4. In the Realm of Aerial Deception: UAV Classification via ISAR Images and Radar Digital Twins for Enhanced Security;IEEE Sensors Letters;2024-07
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