Flying Objects Classification Based on Micro–Doppler Signature Data From UAV Borne Radar

Unmanned aerial vehicles (UAVs) have been widely used in many facets of contemporary society over the past ten years due to their accessibility and affordability. The rise in drone usage brings up privacy and security issues. It is essential to be vigilant for unauthorized UAVs in restricted areas. In this work, a hybrid Convolutional Neural Network-Shuffled Frog Leap (CNN-SFL) algorithm is proposed for classifying various flying objects, such as drones, helicopters, and artificial birds based on micro-Doppler signature (MDS) collected from HB100 radar mounted on UAV. Various array positioning and configuration, such as uniform linear array (ULA), uniform rectangular array (URA), and uniform circular array (UCA), are taken into account when analyzing the accuracy for avoiding performance loss due to a significant angle of arrival (AoA) of the received signal. Further, the activities of drones are also classified, and accuracy is assessed in comparison to existing algorithms. The results demonstrate that the proposed technique outperforms in all cases. In the endfire direction, URA performs better as compared to the other configurations and in other directions, ULA performs better.