Classification of UAVs using Time-Frequency Analysis of Remote Control Signals and CNN (IEEE International Symposium on Smart Electronic Systems (iSES), 2022)
Abstract:
Unmanned aerial vehicles (UAVs) have recently gained a significant interest in the research community owing to their unrivaled commercial chances in wireless communications, search and rescue, surveillance, logistics, delivery, and intelligent agriculture. In safety critical applications such as intrusions, identifying the type of drone enhances the countermeasures. This paper proposes classifying UAVs from radio frequency (RF) fingerprints using time-frequency transformation and convolutional neural networks (CNN). The proposed methodology involves RF fingerprints’ wavelet synchrosqueezed transform (WSST) followed by a proposed lightweight CNN model. The methodology is verified on a data set containing fifteen different classes of drone’s RF fingerprint. The proposed CNN model size, Raspberry Pi deployment feasibility, and accuracy are compared with the existing pre-trained state-of-art deep learning models. The proposed model achieves a testing accuracy of 99.09% at 387 kilobytes (KB) size and can run on Raspberry Pi in 25.54 milliseconds.