基于EEMD与SVD相结合的弱信号提取方法
发布时间：2019-08-26 10:36
【摘要】：提出了基于集合经验模态分解(EEMD)和奇异值(SVD)相结合的微弱信号提取方法和高低频部分的判别准则:采用EEMD把信号分解成几部分,将IMF分为高频段与低频段2部分,对2部分分别进行奇异值处理,叠加得到降噪信号,做出其频谱图,得到所需要的有用信号。此方法可以在未知原信号的情况下提取,并且可以提取信噪比为-15 dB的信号。仿真结果和对比分析表明,此方法能更好地提取微弱特征信号。
[Abstract]:A weak signal extraction method based on ensemble empirical mode decomposition (EEMD) and singular value (SVD) and a criterion for distinguishing high and low frequency components are presented. The signal is decomposed into several parts by using EEMD, and the IMF is divided into high frequency band and low frequency section 2, and the two parts are respectively subjected to singular value processing. The noise reduction signal is superposed to obtain the noise reduction signal, and the spectrum diagram thereof is made to obtain the required useful signal. The method can be extracted in the case of an unknown original signal, and a signal with a signal-to-noise ratio of -15dB can be extracted. The simulation results and the comparative analysis show that the method can better extract the weak characteristic signal.
【作者单位】： 西安建筑科技大学机电工程学院;
【基金】：国家自然科学基金青年科学基金项目(51105292) 教育部博士点基金项目(20126120110009) 陕西省科技攻关项目(2013K07-09) 陕西省教育厅专项基金项目(2013JK1032)
【分类号】：TH17
本文编号：2529222
[Abstract]:A weak signal extraction method based on ensemble empirical mode decomposition (EEMD) and singular value (SVD) and a criterion for distinguishing high and low frequency components are presented. The signal is decomposed into several parts by using EEMD, and the IMF is divided into high frequency band and low frequency section 2, and the two parts are respectively subjected to singular value processing. The noise reduction signal is superposed to obtain the noise reduction signal, and the spectrum diagram thereof is made to obtain the required useful signal. The method can be extracted in the case of an unknown original signal, and a signal with a signal-to-noise ratio of -15dB can be extracted. The simulation results and the comparative analysis show that the method can better extract the weak characteristic signal.
【作者单位】： 西安建筑科技大学机电工程学院;
【基金】：国家自然科学基金青年科学基金项目(51105292) 教育部博士点基金项目(20126120110009) 陕西省科技攻关项目(2013K07-09) 陕西省教育厅专项基金项目(2013JK1032)
【分类号】：TH17
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，本文编号：2529222
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