Classification of birds and drones by exploiting periodical motions in Doppler spectrum series
Author:
Affiliation:
1. School of Electronics and Communication, Sun Yat-Sen University,Shenzhen,China,518107
2. Beijing Institute of Tracking and Telecommunication Technology,Beijing,China,100854
3. Armed Police Engineering University,Xi'an,China,510507
Funder
National Natural Science Foundation of China
Aeronautical Science Foundation of China
Pearl River Talent Recruitment Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
http://xplorestaging.ieee.org/ielx7/5971804/10066212/10043786.pdf?arnumber=10043786
Reference25 articles.
1. Improved Drone Classification Using Polarimetric Merged-Doppler Images
2. Classification of Birds and UAVs Based on Radar Polarimetry
3. Feature extraction and radar track classification for detecting UAVs in civilian airspace;mohajerin;Proc of the IEEE Radar Conference,0
4. Track-Feature-Based Target Classification in Passive Radar for Low-Altitude Airspace Surveillance
5. Theoretical and Experimental Analysis of Radar Micro-Doppler Signature Modulated by Rotating Blades of Drones
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1. Inductive Conformal Prediction Enhanced LSTM-SNN Network: Applications to Birds and UAVs Recognition;IEEE Geoscience and Remote Sensing Letters;2024
2. A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction;IET Radar, Sonar & Navigation;2023-11-27
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