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基于独立分量谱相关方法的重力固体潮信号潮汐谐波分析
引用本文:矣昕宝,魏巍,全海燕.基于独立分量谱相关方法的重力固体潮信号潮汐谐波分析[J].华中师范大学学报(自然科学版),2019,53(2):207-213.
作者姓名:矣昕宝  魏巍  全海燕
作者单位:1.昆明理工大学信息工程与自动化学院, 昆明 650500;2.西双版纳职业技术学院师范学院(公课部), 云南 西双版纳 666100
摘    要:为了提取重力固体潮信号各谐波分量间能量差异较大的谐波分量,该文在利用独立分量分析实现重力固体潮信号的加性分解,再利用谱相关分析独立分量间的谱相关特性进行分析,揭示了各谐波间的调制关系.同时,通过对独立分量间的自相关谱和互相关谱的分析,论证了在自相关谱中,能量大的谐波分量被凸显,能量小的谐波分量被抑制,而在互相关谱中,能量较小的谐波分量能更好地凸显出来.结果表明,互相关谱更能够揭示重力固体潮信号中弱的潮汐谐波分量及其调制关系.

关 键 词:重力固体潮    独立分量分析    循环自相关    循环互相关  
收稿时间:2019-04-11

Analysis of gravity earth tide signals based on spectral correlation method
YI Xinbao,WEI Wei,QUAN Haiyan.Analysis of gravity earth tide signals based on spectral correlation method[J].Journal of Central China Normal University(Natural Sciences),2019,53(2):207-213.
Authors:YI Xinbao  WEI Wei  QUAN Haiyan
Institution:1.Faculty of Information and Automation, Kunming University of Science and Technology, Kunming 650500, China;2.Xishuangbanna Vocational Technical College Teachers College (Public Section), Xishuangbanna, Yunnan 666100, China
Abstract:In order to extract the harmonic components whose energy varies greatly among the harmonic components of gravity solid tidal signals, this paper uses independent component analysis to realize the additive decomposition of gravity solid tidal signals, and then uses spectral correlation analysis to analyze the spectral correlation characteristics of independent components. The modulation relationship between the harmonics is revealed. At the same time, through the analysis of the autocorrelation spectrum and the correlation spectrum between independent components, it is demonstrated that in the autocorrelation spectrum, the high-energy harmonic component is highlighted, and the low-energy harmonic component is suppressed, and in the cross-spectrum, Harmonic components with less energy can be better highlighted. The results show that the cross-spectrum can reveal the weak tidal harmonic component and its modulation relationship in gravity solid tidal signal.
Keywords:gravity solid tide  independent component analysis  cycle autocorrelation  cycle intercorrelation  
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