【摘要】：矢量水聽器是在標(biāo)量水聽器基礎(chǔ)上發(fā)展的一種新形式,以其在定位定向方面優(yōu)于標(biāo)量水聽器而成為研究的熱門方向,再將微機(jī)電系統(tǒng)(MEMS)技術(shù)應(yīng)用于矢量水聽器更是一種創(chuàng)新方法和創(chuàng)新原理的嘗試。MEMS矢量水聽器具有矢量性、體積小、一致性好和可批量生產(chǎn)等優(yōu)勢(shì)。隨著科學(xué)技術(shù)的不斷發(fā)展,MEMS矢量水聽器種類日趨繁多并且其性能也逐漸變得成熟,但其在接收信號(hào)數(shù)據(jù)時(shí)仍會(huì)混入噪聲,為能更好地進(jìn)行下一步的目標(biāo)定向定位或是成像研究,需要先對(duì)水聽器陣列信號(hào)進(jìn)行去噪處理。本文系統(tǒng)地研究了不同的模態(tài)分解方法在MEMS矢量水聽器信號(hào)去噪及應(yīng)用。利用仿真信號(hào)數(shù)據(jù)和中北大學(xué)國(guó)防重點(diǎn)實(shí)驗(yàn)室在汾河進(jìn)行的汾機(jī)實(shí)測(cè)數(shù)據(jù)驗(yàn)證了不同模態(tài)分解的去噪效果和性能指標(biāo)。論文主要研究的內(nèi)容包括:(1)傳統(tǒng)的信號(hào)去噪方法,如傅里葉變換法、自適應(yīng)去噪法和形態(tài)濾波法等,在應(yīng)用于水聲微弱信號(hào)中都有一定的去噪效果,但也存在一定的不足之處。本文利用模態(tài)分解方法對(duì)含噪信號(hào)分解的直觀性和易實(shí)現(xiàn)性,先對(duì)仿真含噪信號(hào)進(jìn)行分解處理,然后根據(jù)模態(tài)分解方法的去噪原理對(duì)分解信號(hào)進(jìn)行去噪處理,得到了不同方法對(duì)仿真信號(hào)的去噪效果和性能指標(biāo),對(duì)比仿真實(shí)驗(yàn)的去噪效果和性能指標(biāo)得出變分模態(tài)分解方法較一系列的經(jīng)驗(yàn)?zāi)B(tài)分解方法更優(yōu)。(2)由于經(jīng)驗(yàn)?zāi)B(tài)分解方法在分解含噪信號(hào)時(shí)所產(chǎn)生的模態(tài)混疊影響,導(dǎo)致在選取固有模態(tài)函數(shù)時(shí)會(huì)產(chǎn)生信號(hào)失真和去噪效果差的問題,本文通過對(duì)分解后的固有模態(tài)函數(shù)進(jìn)行有針對(duì)性的再去噪處理來提升算法的去噪能力。根據(jù)信號(hào)處理的基本知識(shí),隨機(jī)噪聲基本都處于高頻部分,將含有大部分噪聲的固有模態(tài)函數(shù)直接舍去或是再利用小波閾值去噪和小波包去噪分別處理有明顯信號(hào)的固有模態(tài)函數(shù),進(jìn)一步提升去噪效果和降低信號(hào)失真。(3)由于汾機(jī)實(shí)測(cè)數(shù)據(jù)的復(fù)雜性,只利用經(jīng)驗(yàn)?zāi)B(tài)分解方法不能很好地去除實(shí)測(cè)數(shù)據(jù)中的噪聲。根據(jù)對(duì)汾機(jī)實(shí)測(cè)數(shù)據(jù)的頻譜分析,得到實(shí)測(cè)數(shù)據(jù)不僅有高頻的隨機(jī)噪聲,而且還有低頻的漂移干擾。結(jié)合實(shí)際問題需要,本文將經(jīng)驗(yàn)?zāi)B(tài)分解方法與小波去噪相結(jié)合的方法應(yīng)用到實(shí)測(cè)數(shù)據(jù)去噪處理中,得到了源余弦信號(hào)的較好恢復(fù),進(jìn)而在一定程度上降低了模態(tài)混疊的影響。最后,再利用變分模態(tài)分解算法對(duì)實(shí)測(cè)數(shù)據(jù)進(jìn)行去噪處理,得出該方法很好地解決了經(jīng)驗(yàn)?zāi)B(tài)分解方法在分解含噪信號(hào)時(shí)所產(chǎn)生的模態(tài)混疊影響,并且在去噪效果上比經(jīng)驗(yàn)?zāi)B(tài)分解和小波結(jié)合方法更有優(yōu)越性。
[Abstract]:Vector hydrophone is a new form developed on the basis of scalar hydrophone. It is a hot research direction because it is superior to scalar hydrophone in orientation and orientation. The application of MEMS (MEMS) technology to vector hydrophone is an attempt of innovation method and principle. MEMS vector hydrophone has the advantages of vector, small size, good consistency and batch production. With the development of science and technology, the MEMS vector hydrophone is becoming more and more diverse and its performance is becoming mature, but it will still mix with noise when it receives the signal data, so it can better carry out the target orientation or imaging research in the next step. First, the hydrophone array signal needs to be de-noised. In this paper, different mode decomposition methods for MEMS vector hydrophone signal denoising and their applications are systematically studied. The denoising effect and performance index of different modal decomposition are verified by using the simulated signal data and the measured data of Fen machine carried out by the National Defense key Laboratory of Zhongbei University in Fenhe River. The main contents of this paper are as follows: (1) the traditional signal denoising methods, such as Fourier transform method, adaptive denoising method and morphological filtering method, have a certain denoising effect in weak underwater acoustic signals, but there are still some shortcomings. In this paper, the method of mode decomposition is used to decompose the noisy signal directly and easily. Firstly, the simulated noisy signal is decomposed, and then the decomposed signal is de-noised according to the principle of the modal decomposition method. The de-noising effect and performance index of different methods for simulation signal are obtained. Comparing the denoising effect and performance index of the simulation experiment, it is concluded that the variational mode decomposition method is better than a series of empirical mode decomposition methods. (2) because of the modal aliasing effect of the empirical mode decomposition method when decomposing the noisy signal, The problem of signal distortion and poor denoising effect will occur when selecting the inherent mode function. In this paper, the de-noising ability of the algorithm is improved by rescinding the decomposed inherent mode function. According to the basic knowledge of signal processing, random noise is basically in the high frequency part, The inherent mode function with most noise is removed directly or the inherent mode function with obvious signal is processed by wavelet threshold denoising and wavelet packet denoising respectively. (3) because of the complexity of the measured data of Fen machine, only the empirical mode decomposition method can not remove the noise in the measured data. According to the spectrum analysis of the measured data, the measured data have not only high frequency random noise, but also low frequency drift interference. In this paper, the empirical mode decomposition method combined with wavelet de-noising method is applied to the real data denoising processing. The better recovery of source cosine signal is obtained, and the influence of modal aliasing is reduced to a certain extent. Finally, the variational mode decomposition algorithm is used to Denoise the measured data, and it is concluded that the empirical mode decomposition method can solve the modal aliasing effect when decomposing the noisy signal. And the denoising effect is better than the empirical mode decomposition and wavelet combination method.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TB565.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 赫彬;張雅婷;白艷萍;;基于ICA-CEEMD小波閾值的傳感器信號(hào)去噪[J];振動(dòng)與沖擊;2017年04期

2 趙迎;樂友喜;黃健良;王姣;劉陳希;劉兵卿;;CEEMD與小波變換聯(lián)合去噪方法研究[J];地球物理學(xué)進(jìn)展;2015年06期

3 孫玉;胡博;王曉春;;國(guó)際矢量水聽器技術(shù)發(fā)展態(tài)勢(shì)文獻(xiàn)計(jì)量分析[J];情報(bào)探索;2015年11期

4 韓慶陽;孫強(qiáng);王曉東;李丙玉;高群;;CEEMDAN去噪在拉曼光譜中的應(yīng)用研究[J];激光與光電子學(xué)進(jìn)展;2015年11期

5 李軍;李青;;基于CEEMDAN-排列熵和泄漏積分ESN的中期電力負(fù)荷預(yù)測(cè)研究[J];電機(jī)與控制學(xué)報(bào);2015年08期

6 劉長(zhǎng)良;武英杰;甄成剛;;基于變分模態(tài)分解和模糊C均值聚類的滾動(dòng)軸承故障診斷[J];中國(guó)電機(jī)工程學(xué)報(bào);2015年13期

7 唐貴基;王曉龍;;參數(shù)優(yōu)化變分模態(tài)分解方法在滾動(dòng)軸承早期故障診斷中的應(yīng)用[J];西安交通大學(xué)學(xué)報(bào);2015年05期

8 王姣;李振春;王德營(yíng);;基于CEEMD的地震數(shù)據(jù)小波閾值去噪方法研究[J];石油物探;2014年02期

9 方爾正;洪連進(jìn);楊德森;;MEMS型水聽器的自噪聲分析[J];哈爾濱工程大學(xué)學(xué)報(bào);2014年03期

10 席旭剛;朱海港;羅志增;;基于EEMD和二代小波變換的表面肌電信號(hào)消噪方法[J];傳感技術(shù)學(xué)報(bào);2012年11期

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