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基于周期截断数据矩阵奇异值分解的干扰抑制技术 被引量:7

Interference Suppression Technology Based on Singular Value Decomposition of Periodic Truncated Data Matrix
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摘要 针对现有适用于单天线接收机的干扰抑制技术难以为周期调频(PFM)干扰和卫星导航信号提供足够分离度,导致消除干扰成分时卫星导航信号损伤较大的问题,该文提出一种基于周期截断数据矩阵奇异值分解的干扰抑制方法。利用调频干扰信号的周期性把分散在较大带宽的能量集中到重排数据中几个甚至单个频点;进而采用奇异值分解(SVD)将干扰与期望信号映射进不同的投影子空间以消除干扰成分。仿真结果表明该方法可以降低在剔除干扰时卫星导航信号损失,提升卫星导航接收机对抗宽带周期调频干扰的能力。 The separation between the Periodic Frequency Modulation(PFM)interference and satellite navigation signal is not enough in the traditional transform domain,which causes severe satellite navigation signal degradation is suppressing interference.To solve this problem,a data rearrangement method based on period truncation is proposed.Using the periodicity of the PFM interfering signal,the energy scattered in a larger bandwidth is concentrated to a single frequency point in the rearranged data.Then,Singular Value Decomposition(SVD)is adopted to map the interference and the desired signal into different projection subspaces to eliminate the interference components.The simulation results show that the proposed method can reduce the overlap degree of the PFM interference and satellite navigation signals,and reduce the damage of satellite navigation signals.
作者 戚连刚 申振恒 王亚妮 国强 Kaliuzhny Mykola QI Liangang;SHEN Zhenheng;WANG Yani;GUO Qiang;Kaliuzhny Mykola(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Agile and Intelligent Computing Key Laboratory of Sichuan Province,Chengdu 610036,China;Kharkiv National University of Radio Electronics,Kharkiv 61166,Ukraine;Key Laboratory of Advanced Marine Communication and InformationTechnology,Ministry of Industry and Information Technology,Harbin 150001,China)
出处 《电子与信息学报》 EI CSCD 北大核心 2022年第6期2143-2150,共8页 Journal of Electronics & Information Technology
基金 国家重点研发计划(2018YFE0206500) 国家自然科学基金(62071140,62101155)。
关键词 卫星导航接收机 周期调频干扰抑制 周期截断数据矩阵 奇异值分解 Satellite navigation receiver Periodic Frequency Modulation(PFM)interference suppression Periodic truncated data matrix Singular Value Decomposition(SVD)
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