Convolutional neural networks and wavelets for drone classification

Abstract

Over recent years, drone identification and detection has become an increasing concern for public safety and security. In this paper, we explore the use of convolutional neural networks (CNNs) applied to the continuous and discrete wavelet transform (CWT/DWT) scalogram of reflected radar signals from drones. In particular, we use the Martin-Mulgrew model to simulate the radar signals reflected off of five different types of drones from an X-band and W-band radar. The drones have different blade lengths and blade rotation rates, and these parameters will affect their respective scalograms, allowing for the use of CNNs in this classification problem. Results with real radar data sets collected in the laboratory are also presented.