射电天文台址干扰的CTS特征识别方法  

CTS features based electromagnetic interference identification at radio observatory site

作　　者：王丹洋 朴春莹 刘奇 关磊 李赞[1] WANG Danyang;PIAO Chunying;LIU Qi;GUAN Lei;LI Zan(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China;Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China;Xinjiang Key Laboratory of Microwave Technology,Urumqi 830011,China)

机构地区：[1]西安电子科技大学通信工程学院,陕西西安710071 [2]中国科学院新疆天文台,新疆维吾尔自治区乌鲁木齐830011 [3]新疆微波技术重点实验室,新疆维吾尔自治区乌鲁木齐830011

出　　处：《西安电子科技大学学报》2025年第1期80-93,共14页Journal of Xidian University

基　　金：国家重点研发计划(2021YFC2203503);中央高校基础科研业务费(QTZX23065);国家自然科学基金(62425103,62121001)。

摘　　要：无线电技术的蓬勃发展致使射电天文台址频繁遭遇射频电磁干扰,进而导致天文观测数据受到污染。针对射频干扰的时序非连续特性导致以原始IQ数据及其提取的统计特征作为残差神经网络输入的方法出现损失函数收敛难和识别准确率低的难题,文中提出了基于复合时间尺度特征的射频干扰识别方法。首先,在时频域进行高维映射以揭示数据隐含信息,并融合长短时特征以提升描述多样性。其次,搭建射频电磁干扰识别网络,网络包括三部分:深度卷积神经网络实现特征高效抽取;路径聚合网络融合浅层图形与深层语义特征;预测输出层则整合多尺度特征进行最终识别判断。实验结果表明,文中所提方法整体精度达到96%,相比于以原始IQ信号作为神经网络输入的整体准确率提升超过30%,有效解决了信号的时序非连续特性导致神经网络难以训练、性能较差的问题。The swift advancement of radio technology has frequently introduced radio frequency interference(RFI)at the radio observatory site,thereby contaminating the data collected from astronomical observations.When the original IQ data and the corresponding statistical features of RFI are used as inputs to a residual neural network,the temporal discontinuity of RFI hinders the convergence of loss functions,which also diminishes the recognition accuracy.To address the challenges,this paper proposes a radio frequency interference identification method based on composite time-scale features.First,the hidden information on the data is revealed through high-dimensional mapping in the time-frequency domain,whose descriptive diversity is enhanced by the fusion of both long and short time features.Second,an RFI recognition network is constructed,which consists of three parts:a deep convolutional neural network for efficient feature extraction;a path aggregation network for combining shallow graphical features with deep semantic features;a predictive output network that integrates multi-scale features for making a decision for recognition.Experimental results show that the overall recognition accuracy of the proposed method achieves 96%,representing an improvement exceeding 30%over that obtained by using the original IQ signal as the neural network input.Therefore,the method proposed in this paper effectively addresses the issue of neural networks being difficult to train and exhibiting poor performance due to the temporal discontinuity of the signals.

关 键 词：射频干扰 神经网络 干扰信号 信号识别 

分 类 号：TN914.42[电子电信—通信与信息系统]