Convolutional Neural Networks for Robust Classification of Drones
Author:
Affiliation:
1. University of Birmingham,Microwave Integrated Systems Laboratory,Birmingham,UK
2. Aveillant Ltd,Cambridge,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9764147/9763897/09764172.pdf?arnumber=9764172
Reference19 articles.
1. SBR-dependent drone classification using convolutional neural networks;dale;IET Radar Sonar & Navigation,2021
2. Robust Drone Classification Using Two-Stage Decision Trees and Results from SESAR SAFIR Trials
3. Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar
4. Classification of drones and birds using convolutional neural networks applied to radar micro‐Doppler spectrogram images
5. Examination of Drone Micro-Doppler and JEM/HERM Signatures
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3. Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler;2023 IEEE International Radar Conference (RADAR);2023-11-06
4. Fast classification of drones and birds with an LSTM network applied to 1D phase data;2023 IEEE International Radar Conference (RADAR);2023-11-06
5. A Bayesian track management scheme for improved multi‐target tracking and classification in drone surveillance radar;IET Radar, Sonar & Navigation;2023-08-23
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