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| Efficient Classification of Birds and Drones Considering Real Observation Scenarios Using FMCW Radar |
| Se-Won Yoon, Soo-Bum Kim, Joo-Ho Jung, Sang-Bin Cha, Young-Seok Baek, Bon-Tae Koo, In-Oh Choi, Sang-Hong Park |
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J. Electromagn. Eng. Sci. 2021;21(4):270-281. Published online September 30, 2021 DOI: https://doi.org/10.26866/jees.2021.4.r.34 |
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Efficient Classification of Birds and Drones Considering Real Observation Scenarios Using FMCW Radar Journal of Electromagnetic Engineering and Science. 2021;21(4):270-281  Detection of the Altitude and On-the-Ground Objects Using 77-GHz FMCW Radar Onboard Small Drones Drones. 2023;7(2):86 Target Classification Using Combined YOLO-SVM in High-Resolution Automotive FMCW Radar 2020 IEEE Radar Conference (RadarConf20). 2020; Classification of drones and birds using convolutional neural networks applied to radar micro‐Doppler spectrogram images IET Radar, Sonar & Navigation. 2020;14(5):653-661 Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios Sensors. 2020;20(22):6505 Moving Target Classification with a Dual Automotive FMCW Radar System Using Convolutional Neural Networks 2021 21st International Radar Symposium (IRS). 2021; FMCW Radar Prototype Development for Detection and Classification of Nano-Targets 2020 IEEE International Radar Conference (RADAR). 2020; LSTM Based Classification of Targets using FMCW Radar Signals 2021 29th Signal Processing and Communications Applications Conference (SIU). 2021; An Initial Investigation into Using Convolutional Neural Networks for Classification of Drones 2020 IEEE International Radar Conference (RADAR). 2020; Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images Signals. 2023;4(2):337-358 |
