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Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data

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Bernard_Cooper_2022_Multiple_drone_type_SPIE_121080A.pdf (975.4Kb)
Date
27/05/2022
Author
Bernard-Cooper, Joshua
Rahman, Samiur
Robertson, Duncan A.
Keywords
Radar
Drone
Machine learning
Doppler effect
Feature extraction
Classification systems
Principal component analysis
Millimeter wave sensors
Unmanned aerial vehicles
QA75 Electronic computers. Computer science
QC Physics
NS
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Abstract
Systems designed to detect the threat posed by drones should be able to both locate a drone and ideally determine its type in order to better estimate the level of threat. Previously, drone types have been discriminated using millimeter-wave Continuous Wave (CW) radar, which produces high quality micro-Doppler signatures of the drone propeller blades with fully sampled Doppler spectra. However, this method is unable to locate the target as it cannot measure range. By contrast, Frequency Modulated Continuous Wave (FMCW) data typically undersamples the micro-Doppler signatures of the blades but can be used to locate the target. In this paper we investigate FMCW features of four drones and if they can be used to discriminate the models using machine learning techniques, enabling both the location and classification of the drone. Millimeter-wave radar data are used for better Doppler sensitivity and shorter integration time. Experimentally collected data from Ttree quadcopters (DJI Phantom Standard 3, DJI Inspire 1, and Joyance JT5L-404) and a hexacopter (DJI S900) have been. For classification, feature extraction based machine learning was used. Several algorithms were developed for automated extraction of micro-Doppler strength, bulk Doppler to micro-Doppler ratio, and HERM line spacing from spectrograms. These feature values were fed to classifiers for training. The four models were classified with 85.1% accuracy. Higher accuracies greater than 95% were achieved for training using fewer drone models. The results are promising, establishing the potential for using FMCW radar to discriminate drone types.
Citation
Bernard-Cooper , J , Rahman , S & Robertson , D A 2022 , Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data . in K I Ranney & A M Raynal (eds) , Proceedings of SPIE : Radar Sensor Technology XXVI . vol. 12108 , 121080A , Proceedings of SPIE , vol. 12108 , SPIE , SPIE Defense + Commercial Sensing , Orlando , Florida , United States , 3/04/22 . https://doi.org/10.1117/12.2618026
 
conference
 
Publication
Proceedings of SPIE
DOI
https://doi.org/10.1117/12.2618026
ISSN
0277-786X
Type
Conference item
Rights
Copyright © 2022 SPIE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://doi.org/10.1117/12.2618026.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/26232

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