【摘要】：提出了基于集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)和奇異值(SVD)相結(jié)合的微弱信號(hào)提取方法和高低頻部分的判別準(zhǔn)則:采用EEMD把信號(hào)分解成幾部分,將IMF分為高頻段與低頻段2部分,對(duì)2部分分別進(jìn)行奇異值處理,疊加得到降噪信號(hào),做出其頻譜圖,得到所需要的有用信號(hào)。此方法可以在未知原信號(hào)的情況下提取,并且可以提取信噪比為-15 dB的信號(hào)。仿真結(jié)果和對(duì)比分析表明,此方法能更好地提取微弱特征信號(hào)。
[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.
【作者單位】： 西安建筑科技大學(xué)機(jī)電工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金青年科學(xué)基金項(xiàng)目(51105292) 教育部博士點(diǎn)基金項(xiàng)目(20126120110009) 陜西省科技攻關(guān)項(xiàng)目(2013K07-09) 陜西省教育廳專項(xiàng)基金項(xiàng)目(2013JK1032)
【分類號(hào)】：TH17

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