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  • 黄伟庆,王元坤,张巧遇,李静,魏冬.基于稀疏分量分析的海量短波电台快速自动识别方法[J].信息安全学报,已采用    [点击复制]
  • huangweiqing,wangyuankun,zhangqiaoyu,lijing,weidong.A Fast Automatic Identification Method of Massive Shortwave Radio Stations Based on Sparse Component Analysis[J].Journal of Cyber Security,Accept   [点击复制]
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基于稀疏分量分析的海量短波电台快速自动识别方法
黄伟庆, 王元坤, 张巧遇, 李静, 魏冬
0
(中国科学院信息工程研究所)
摘要:
针对短波频段内海量电台的快速自动识别问题,本文提出一种基于稀疏分量分析的快速识别方法,该方法基于各电台发射信号时域所具有的独特周期性及稀疏性,利用高速频谱扫描数据对各信道的多个电台进行自动分离和识别。为了对短波时变信道衰落下的电台信号进行分离,提出一种基于时间特征聚类的稀疏分量分析的算法,该算法将时间特征与幅值特征相融合进行聚类,实现对混合矩阵的估计。此外,根据聚类结果,该算法将信号向类心向量进行投影,去除时变信道衰落引入的噪声。在不同播放时间,不同占空比和不同周期的仿真实验中,该算法的识别正确率为98.1%,相比聚类稀疏分量分析和快速独立成分分析分别提升了7.3%和16.8%,能够很好解决短波电台分离和识别的问题。
关键词:  短波电台  信号识别  信号分离  稀疏分量分析  无线电监测
DOI:10.19363/J.cnki.cn10-1380/tn.2023.08.13
投稿时间:2021-02-04修订日期:2021-03-25
基金项目:
A Fast Automatic Identification Method of Massive Shortwave Radio Stations Based on Sparse Component Analysis
huangweiqing, wangyuankun, zhangqiaoyu, lijing, weidong
(Institute of Information Engineering, Chinese Academy of Sciences)
Abstract:
Aiming at the problem of rapid automatic identification of a large number of radio stations in the high frequency band, a fast identification method based on sparse component analysis is proposed. Based on the unique periodicity and sparseness of each station’s transmitted signal in the time domain, the high-speed spectrum scanning data are used to separate and identify multiple radio stations on each channel automatically. In order to separate the radio signals under shortwave time-varying channel fading, a sparse component analysis algorithm based on time feature clustering is proposed, in which clustering is performed with both the time features and the amplitude features to realize the estimation of the mixing matrix. In addition, according to the clustering results, the signals are projected onto the vectors passing through the clustering centers to remove the noise introduced by the time-varying channel fading. In simulation experiments with different broadcast times, different duty cycles, and different periods, the algorithm’s accuracy rate of station identification is 98.1%, which is 7.3% and 16.8% higher than clustering based sparse component analysis and fast independent component analysis, respectively, providing a good solution to the problem of separation and identification of shortwave radio stations.
Key words:  shortwave radio stations  signal identification  signal separation  sparse component analysis  radio monitoring