Machine Learning for UAV Classification Employing Mechanical Control Information
Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, a drone's range-doppler signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if the effect of the mechanical control system wasn't taken into consideration, which may lead to a non-unique signature of a drone while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of a quadcopter and a hexacopter UAVs on their range-doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the two UAVs and its accuracy is found to exceed 90%.
This work was supported in part by NSERC No. 55059
Email Address of Submitting Authoransayed@uwaterloo.ca
ORCID of Submitting Author0000-0003-3821-0487
Submitting Author's InstitutionUniversity of Waterloo
Submitting Author's Country