Classification of Small Radar Cross Section Targets with Convolutional Neural Networks (CNNs)

Khattab, Esalm; Omara, Ahmed; Ahmed, Fathy Mohammed; Fouad, Ahmed

doi:10.21608/iugrc.2021.246358

Classification of Small Radar Cross Section Targets with Convolutional Neural Networks (CNNs)

Document Type : Original Article

Authors

¹ Military Technical College, Egypt.

² Assoc.Prof., Military Technical college, Egypt.

³ Military Technical college, Egypt.

10.21608/iugrc.2021.246358

Abstract

In the recent years, drones were used widely in many useful applications as civil, medical, agriculture and military and made a big success in these applications. This made evil people to use drones in some malicious applications which are forbidden by the law. So, nowadays, classification of drones is one of the most important objectives for the researchers to decrease crimes made by these drones. Classification of drones, nowadays, is made using radars due to it is working without respect to the weather, so the radars must be trained for this work. The best way to train the radars is Artificial Intelligence specially with CNNs Deep Learning method which select the target features itself without needing to human interference. Also, as known that the RCS of drones is comparable with birds and this leads researchers to create much more accurate algorithms to have the best classification accuracy. In this paper we used 17000 samples for classification, 16000 for radar training and 1000 for testing.