RF Signal Classification-based Drone Identification with Multimodal Database

The aim of this paper is to conduct a comprehensive review of existing UAV classification and identification methods. We will analyze the evolution of these methods and propose an anti-UAV RF detection and identification technique based on a self-constructed UAV RF signal dataset. This dataset utilizes a Universal Software Radio Peripheral (USRP) to capture the communication RF signals of 24 UAVs within an urban environment. Subsequently, we will undertake feature extraction and time-frequency signal statistical analysis of various UAV RF signals. We will employ machine learning methods for Classification-based Drone Identification. Our ultimate objective is to achieve UAV signal identification and classification using optimization techniques within a reasonable time frame.