Drones Classification based on Millimeter Wave Radar Cross Section via Deep Learning

The wide use of drones is playing a vital role in the era of low-altitude economy, but meanwhile its misuse could also presents a threat to the privacy, property damage, health and public safety. Therefore, there is a timely need to enhance the capability to detect and recognize the flying drones, which however is challenging owing to the small size, slow speed and low altitude of small drones. In the 5G and beyond, the widely deployed base stations are becoming more and more advanced, especially with the large spectrum like the millimetre wave (mmWave) frequency band. This provides a great potential to turn the network of base stations into a network of mmWave radars for the drones' detection by leveraging the integrated sensing and communication (ISAC) techniques. This work aims to classify the drones based on their mmWave radar cross section (RCS) real data that will be converted to two-dimensional (2D) RCS images. Thus, 2D Convolutional Neural Network (CNN) is applied to the image sets to achieve the drone classification. The satisfactory testing results verify the proposed drone classification method.