Convolutional neural networks and wavelets for drone classification

Emily Hunter; Divy Raval; Bhashyam Balaji; Anthony Damini

doi:10.1117/12.2618832

27 May 2022 Convolutional neural networks and wavelets for drone classification

Emily Hunter, Divy Raval, Bhashyam Balaji, Anthony Damini

Proceedings Volume 12108, Radar Sensor Technology XXVI; 121080L (2022) https://doi.org/10.1117/12.2618832
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Emily Hunter, Divy Raval, Bhashyam Balaji, and Anthony Damini "Convolutional neural networks and wavelets for drone classification", Proc. SPIE 12108, Radar Sensor Technology XXVI, 121080L (27 May 2022); https://doi.org/10.1117/12.2618832

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