Classification of drones based on micro-Doppler signatures with dual-band radar sensors

Drone classification has become of great importance due to its increasing popularity and potential threats. The micro-Doppler signatures depending on the rotation of rotor blades allow us to differentiate various types of drones. To enhance the robustness of micro-Doppler based classification of dro...

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Bibliographic Details
Published inPIERS - FALL : 2017 Progress in Electromagnetics Research Symposium - Fall : 19-22 November 2017, Singapore pp. 638 - 643
Main Authors Zhang, Pengfei, Yang, Le, Chen, Gao, Li, Gang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
Drones
Dual band
Feature extraction
Radar
Sensor fusion
Support vector machines
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Summary:Drone classification has become of great importance due to its increasing popularity and potential threats. The micro-Doppler signatures depending on the rotation of rotor blades allow us to differentiate various types of drones. To enhance the robustness of micro-Doppler based classification of drones, a dual band radar classification scheme is proposed in this paper. Firstly, the time-frequency spectrograms are obtained by performing the short-time Fourier Transform (STFT) on the radar data collected by K-band and X-band radar sensors respectively. Then the principal components analysis (PCA) is utilized to extract the features from the time-frequency spectrograms, and the features obtained by the two radar sensors are fused together. Finally, the classification results are obtained by using the Support Vector Machine (SVM). The experimental results show that the classification accuracy obtained by the fusion of dual-band radar sensors is higher than that obtained by using only single radar sensor.
DOI:10.1109/PIERS-FALL.2017.8293214