Efficient UAV Identification Leveraging Multi-Resolution Analysis and Multi-Scale ResNet

As unmanned aerial vehicles (UAVs) become increasingly prevalent in various environments, their detection is essential for ensuring safety and effective management. For non-standard transmitter waveforms, identifying a specific model poses challenges, and most researchers focus on developing deeper and broader architectures to improve performance at the expense of computational burden and speed, which is not suitable for the deployment of UAV radio frequency (RF) fingerprinting identification. In this paper, we propose a novel multi-resolution analysis-based method for UAV identification. We develop a lightweight, multi-scale convolutional network that utilizes various receptive fields to extract unique hardware intrinsic features. We employ the two-dimensional Discrete Wavelet Transform (2D-DWT) to innovatively decompose the low-frequency and the high-frequency components of the RF signal spectrogram into four distinct sub-bands. Simulation results indicate that our proposed methods outperforms other state-of-the-art UAV identification methods in terms of both performance and computational complexity.