FAST无线电干扰智能监测技术

张海燕, 胡宏亮, 王钰, 姜化京, 甘恒谦, 胡浩, 黄仕杰

清华大学学报（自然科学版） ›› 2022, Vol. 62 ›› Issue (11) : 1780-1788.

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清华大学学报（自然科学版） ›› 2022, Vol. 62 ›› Issue (11) : 1780-1788. DOI: 10.16511/j.cnki.qhdxxb.2022.26.030
论文

FAST无线电干扰智能监测技术

  • 张海燕1,3,5, 胡宏亮2, 王钰1,6, 姜化京4, 甘恒谦1,3, 胡浩1, 黄仕杰1
作者信息 +

RFI intelligent monitoring techniques for FAST

  • ZHANG Haiyan1,3,5, HU Hongliang2, WANG Yu1,6, JIANG Huajing4, GAN Hengqian1,3, HU Hao1, HUANG Shijie1
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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.

关键词

FAST / 无线电干扰 / 干扰源识别与定位

Key words

FAST / radio frequency interference / interference identification and localization

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张海燕, 胡宏亮, 王钰, 姜化京, 甘恒谦, 胡浩, 黄仕杰. FAST无线电干扰智能监测技术[J]. 清华大学学报（自然科学版）. 2022, 62(11): 1780-1788 https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.030
ZHANG Haiyan, HU Hongliang, WANG Yu, JIANG Huajing, GAN Hengqian, HU Hao, HUANG Shijie. RFI intelligent monitoring techniques for FAST[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(11): 1780-1788 https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.030

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基金

国家重点研发计划项目（2019YFB1312704）

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