Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images

This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to...

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Bibliographic Details
Published inSignals (Basel) Vol. 4; no. 2; pp. 337 - 358
Main Authors Narayanan, Ram M., Tsang, Bryan, Bharadwaj, Ramesh
Format Journal Article
LanguageEnglish
Published Tokyo MDPI AG 01.06.2023
Subjects
Acoustics
Aviation
bird detection
Birds
Cameras
Classification
continuous-wave radar
Doppler effect
drone detection
Drones
micro-Doppler
Pilots
Radar systems
Radio communications
spectrogram
UAV detection
Unmanned aerial vehicles
Velocity
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ISSN2624-6120
2624-6120
DOI10.3390/signals4020018

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Summary:This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target.
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ISSN:2624-6120
2624-6120
DOI:10.3390/signals4020018