CUAS_Literature_DataSet

The framework for this Literature_DataSet was adopted from (Alzubaidi et al. 2021), starting from the identification, screening, and selection stages, which included over two hundred (205) research papers in the last five years from publishers such as IEEE, Nature, Springer, ACM, Elsevier, and MDPI.
All reviewed papers have tackled the drone classification problem (detection and identification) using RF sensors. Each paper was categorized as a novel approach, survey/review, dataset description, or regulation/standard, with mandatory remarks explaining specific ADRO procedures (e.g., jamming, spoofing, direction finding).

本文献数据集的构建框架借鉴自Alzubaidi等人2021年的研究，遵循文献识别、筛选与遴选流程，涵盖近五年来自IEEE、Nature、Springer、ACM、Elsevier及MDPI等学术出版商的205篇研究论文。
所有纳入审阅的论文均聚焦于基于射频（RF）传感器的无人机分类问题（检测与识别）。每篇文献被划分为新颖方法类、综述/评述类、数据集描述类或规范/标准类，且必须附带针对特定ADRO流程的说明，例如干扰、欺骗、测向。

搜集汇总

背景与挑战

背景概述

CUAS_Literature_DataSet是一个文献数据集，专注于使用RF传感器进行无人机分类（检测和识别）的研究论文。它包含205篇近五年来自IEEE、Nature等出版商的论文，每篇论文被分类为新颖方法、综述、数据集描述或法规标准，并附有ADRO程序（如干扰、欺骗）的备注。该数据集以Excel格式提供，适用于文献综述和无人机相关研究。