ZHANG Haiyan1,3,5, HU Hongliang2, WANG Yu1,6, JIANG Huajing4, GAN Hengqian1,3, HU Hao1, HUANG Shijie1
1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China; 2. Jiangnan Institute of Mechanical and Electrical Design, Guiyang 550009, China; 3. Key Laboratory of FAST, Chinese Academy of Sciences, Beijing 100101, China; 4. Tejin Intelligent Technology Co., Shanghai 201112, China; 5. Hebei Key Laboratory of Radio Astronomy Technology, Shijiazhuang 050081, China; 6. University of Chinese Academy of Sciences, Beijing 100049, China
摘要针对500 m口径球面射电望远镜（five-hundred-meter aperture spherical radio telescope，FAST）电磁环境保护的需求，开展无线电干扰（radio frequency interference，RFI）智能监测技术研究，包括干扰信号的探测识别和干扰源的高精度定位技术。在干扰信号的探测与识别方面，针对FAST台址特性，采用多站点协同频谱感知技术来检测信号，并结合深度神经网络识别信号；在干扰源定位方面，采用基于到达时间差（time difference of arrival，TDOA）的定位技术，通过广义互相关法估计信号到达不同接收机的时间差，实现在低信噪比下的高精度定位。智能监测技术的研究为建立FAST无线电干扰智能监测系统打下基础，并将为FAST周边频谱管理和电磁环境保护工作提供支撑。
Abstract：Radio frequency interference (RFI) intelligent monitoring techniques are being developed for the electromagnetic environment of the five-hundred-meter aperture spherical radio telescope (FAST), including interference source classification and identification and high-precision localization. According to the characteristics of FAST sites, the interference detection and identification uses multi-site cooperative spectrum sensing and signal identification based on deep neural networks. The interference source localization uses time difference of arrival (TDOA) for accurate localization with low signal-to-noise ratios. The time difference between signals arriving at different receivers is estimated using the generalized mutual correlation method. This intelligent monitoring research lays the groundwork for the FAST radio interference intelligent monitoring system as well as the FAST spectrum management and electromagnetic environmental protection work.
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