发表机构
Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述CBRS频段雷达检测技术，对比传统与机器学习类方法，指出其挑战，提出未来或结合两类方法以实现准确快速的检测性能。
AI 中文摘要
3.5 GHz公民宽带无线电服务（Citizens Broadband Radio Service, CBRS）是一种共享无线频段，允许政府系统与商业网络（如专用LTE/5G）使用同一频谱。为防止对关键政府系统（尤其是海军雷达）造成干扰，CBRS采用名为环境感知能力（Environmental Sensing Capability, ESC）的监测系统。ESC如同传感器网络，持续监听雷达信号，检测到时向系统发出警报，以便商业用户临时停止或调整传输。本文综述CBRS频段内的雷达信号检测方法：首先说明监管框架及需识别的雷达信号类型；接着研究能量检测、模式匹配等传统检测技术，并与基于机器学习、深度学习的新方法对比，后者可自动从数据中学习识别雷达信号；还梳理用于评估检测方法的公开数据集与测试平台，以及高检测准确率（如99%检测概率，即雷达重叠召回率）、低延迟（如60秒内）等关键性能要求；最后指出当前挑战，包括虚警、现代无线系统干扰、实时运行需求等。总体而言，本综述表明传统方法在受控环境中简单可靠，而现代学习方法在复杂环境中性能更优，CBRS雷达检测的未来或将结合两类方法，以在实际部署中实现准确、快速、鲁棒的性能。
英文摘要
The 3.5 GHz Citizens Broadband Radio Service (CBRS) is a shared wireless band that allows both government systems and commercial networks (such as private LTE/5G) to use the same spectrum. To prevent interference with critical government systems, especially naval radars, CBRS uses a monitoring system called the Environmental Sensing Capability (ESC). ESC acts like a network of sensors that continuously listens for radar signals and alerts the system when they are detected, so commercial users can temporarily stop or adjust their transmissions. This paper reviews how radar signals are detected within the CBRS band. We first explain the regulatory framework and describe the types of radar signals that need to be identified. We then examine traditional detection methods, such as energy-based and pattern-matching techniques, and compare them with newer approaches based on machine learning and deep learning, which can automatically learn to recognize radar signals from data. We also review publicly available datasets and testing platforms used to evaluate these detection methods, along with key performance requirements such as high detection accuracy (e.g., 99% detection probability (radar overlap recall)) and low delay (e.g., within 60 seconds). Finally, we highlight current challenges, including false alarms, interference from modern wireless systems, and the need for real-time operation. Overall, this survey shows that while traditional methods are simple and reliable in controlled settings, modern learning-based approaches offer better performance in complex environments. The future of CBRS radar detection will likely combine both approaches to achieve accurate, fast, and robust performance in real-world deployments.
