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Classification of Drone Type Using Deep Convolutional Neural Networks Based on Micro- Doppler Simulation

Title
Classification of Drone Type Using Deep Convolutional Neural Networks Based on Micro- Doppler Simulation
Authors
Choi, ByunggilOh, Daegun
DGIST Authors
Oh, Daegun
Issue Date
2018-10-26
Citation
2018 International Symposium on Antennas and Propagation, ISAP 2018
Type
Conference
ISBN
9788957083048
Abstract
Monitoring drones has become an increasingly significant area of study for surveillance and safety purposes. The use of radar is one of the most feasible approaches to detecting a drone as RF waves propagate with a low attenuation constant. In this study, we suggest classifying drones based on the micro-Doppler signatures in the spectrogram when observed by radar. We simulate micro- Doppler signatures from various drones and investigate the feasibility of classifying the type of drone. A deep convolutional neural network is suggested as a classifier and its classification accuracy is reported. © 2018 KIEES.
URI
http://hdl.handle.net/20.500.11750/9699
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Convergence Research Center for Collaborative Robots2. Conference Papers


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