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Extraction of micro-doppler characteristics of drones using high-resolution time-frequency transforms

Title
Extraction of micro-doppler characteristics of drones using high-resolution time-frequency transforms
Authors
Kim, YoungwookNazaroff, MichaelOh, Daegun
DGIST Authors
Oh, Daegun
Issue Date
2018-12
Citation
Microwave and Optical Technology Letters, 60(12), 2949-2954
Type
Article
Article Type
Article
Keywords
Joint time-frequency Analysismicro-Doppler SignaturesTime-frequency MUSICWigner Ville distributionAcoustic noise measurementAircraft detectionComputer musicContinuous wave radarDronesExtractionNetwork securityRadar measurementRadar systemsSpectrographsHigh resolutionJoint time-frequency analysisMicro-DopplerMultiple signal classification algorithmRotating bladesShort time Fourier transformsSmoothed pseudo Wigner Ville distributionTime frequencyWigner-Ville distribution
ISSN
0895-2477
Abstract
The demand for detecting and tracking drones has increased for reasons of surveillance and security. Radar is one of the promising methods in this regard. The recognition and identification of drones using a radar system requires the extraction of their unique micro-Doppler signatures produced by their rotating blades. Because of the blades' rapid rotation speed, difficulties are inherent in visualizing clear micro-Doppler signatures in a conventional joint time-frequency analysis such as the short-time Fourier transform. In this paper, we propose the use of high-resolution transform techniques to visualize the micro-Doppler signatures of drones in a spectrogram. The techniques used include Wigner-Ville distribution, smoothed pseudo-Wigner-Ville distribution, and short-time MUltiple SIgnal Classification (MUSIC) algorithm. In particular, the latter, which had never previously been applied to drones, is suggested to visualize the details of micro-Doppler signatures. We measured three drones using a continuous-wave radar, and performances of these algorithms were compared using data collected from the drones. We could observe that the short-time MUSIC method showed the clearest spectrogram for identifying micro-Doppler signatures. This study can potentially be useful in the field of drone classification. © 2018 Wiley Periodicals, Inc.
URI
http://hdl.handle.net/20.500.11750/9403
DOI
10.1002/mop.31408
Publisher
John Wiley and Sons Inc.
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Collaborative Robots1. Journal Articles


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