Classification of flying object based on radar data using hybrid Convolutional Neural Network-Memetic Algorithm

Abstract

In the last decade, the low cost and easy availability of Unmanned Aerial Vehicles (UAVs) have led to their enormous use in modern society, which leads to privacy and security issues. To keep an eye on the intruder UAV in the restricted area, it needs to classify the other flying objects, such as helicopters, birds, etc. Hence, this work is taken up by considering the Micro-Doppler Signature (MDS) of the flying objects from the different configurations of radar antenna array such as Uniform Linear Array (ULA) and Uniform Rectangular Array (URA). In order to obtain the MDS from the intruder UAV, proper positioning or configuration of the radar antenna array is needed to avoid performance degradation due to the large Angle of Arrival (AoA) of the received signal. A novel Hybrid Convolutional Neural Network-Memetic algorithm is proposed to classify the flying object, which is evaluated for both MDS data collected from the HB100 radar set-up by varying configurations and Real Doppler RAD-DAR (RDRD) existing dataset.