本文選題：信號降噪 + 經(jīng)驗小波變換EWT�。� 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：降噪問題是信號處理領域中的一個經(jīng)典問題,信號與噪聲類型多種多樣,而目前的大多降噪方法都只針對特定的信號與噪聲,對適用性更廣的降噪方法的研究一直是人們努力的方向。自適應濾波降噪盡管需要額外的參考噪聲作為輸入,但對各種噪聲的適應性較強,因此十分具有研究價值。自適應成分分解方法能夠根據(jù)信號自身性質(zhì)將其分解成多個成分,根據(jù)噪聲在不同成分中分布的不同,通過設定閾值或某種方法對各成分進行選擇性的保留與重構,也被廣泛用于降噪研究。為充分利用成分分解方法的自適應性,并克服LMS型自適應濾波算法某些時候收斂速度過慢且對于非平穩(wěn)信號去噪效果不理想的問題,本文主要研究將自適應成分分解方法與自適應濾波器相結合的降噪方法。針對包含多種頻率成分或相關度高的噪聲LMS型算法收斂速度很慢降噪效果差的問題,本文引入了一種成分分解方法——經(jīng)驗小波變換EWT。EWT方法是一種基于對頻譜劃分的自適應頻帶分解方法,通常的頻譜劃分方法是根據(jù)極大極小值點尋找分割邊界,為了應對頻譜更加復雜的信號,本文研究了一種基于尺度空間的無參數(shù)頻譜分割方法。在此基礎上本文提出了基于EWT的自適應濾波降噪方法,該方法先通過EWT將噪聲信號分解成若干子帶,保留過程中得到的經(jīng)驗小波函數(shù)并用其分解混合信號,對每個噪聲信號與混合信號對應的子帶獨立進行自適應濾波降噪,最后對每個子帶的濾波結果進行累加得到最終降噪后的信號。通過實驗研究了EWT中參數(shù)對分割的影響,從重構誤差上看對于周期性確定性信號,其歸一化誤差在10-4數(shù)量級,進一步降低重構誤差將能夠提高降噪效果;分解后子帶信號在頻譜動態(tài)范圍的改善也通過實驗得以驗證。最后進行了信號降噪實驗,仿真實驗可以看出該方法可以取得比直接自適應濾波更好的收斂性能,對于在粉紅噪聲背景下的語音信號降噪效果也更好。針對對于非平穩(wěn)信號/噪聲LMS型算法降噪效果有限的問題,引入另一種成分分解方法——經(jīng)驗模態(tài)分解EMD方法。EMD方法脫離了傅立葉分析框架,通過對信號極值包絡不斷取平均,迭代提取出單分量信號,能更好地反映信號局部特征,適合對非平穩(wěn)信號進行分析。本文首先對兩種其他研究者提出的EMD與自適應濾波結合的降噪方法進行研究,分析其中存在的一些問題,在此基礎上提出采用多元經(jīng)驗模態(tài)分解MEMD對混合信號與參考噪聲信號同步分解,分解后各IMF的平穩(wěn)性得到了加強,更適合使用自適應濾波進行處理,通過降噪實驗驗證了方法的有效性。本文的研究初步提出了將自適應成分分解與自適應濾波結合這一新的降噪思路,從兩個角度出發(fā)分別引入兩種不同的成分分解方法,降噪實驗的結果驗證了這一思路的可行性。
[Abstract]:Noise reduction is a classical problem in the field of signal processing. There are a variety of signal and noise types, but most of the current noise reduction methods only focus on specific signals and noises. The research of more widely used noise reduction methods has been the direction of people's efforts. Adaptive filtering noise reduction needs additional reference noise as input, but it has strong adaptability to all kinds of noise, so it is of great research value. The adaptive component decomposition method can decompose the signal into several components according to its own properties. According to the different distribution of noise in different components, the adaptive component decomposition method can selectively retain and reconstruct each component by setting a threshold or a certain method. It is also widely used in noise reduction research. In order to make full use of the self-adaptability of the component decomposition method, and to overcome the problem that the LMS adaptive filtering algorithm converges too slowly at some times and the denoising effect for non-stationary signals is not satisfactory. This paper mainly studies the noise reduction method which combines the adaptive component decomposition method and the adaptive filter. In order to solve the problem that the convergence speed of noise LMS type algorithm with multiple frequency components or high correlation is very slow, the effect of noise reduction is poor. In this paper, a component decomposition method, empirical wavelet transform (EWT.EWT), is introduced, which is an adaptive frequency band decomposition method based on spectrum partitioning. In order to deal with the more complex spectrum of signals, this paper studies a non-parametric spectrum segmentation method based on scale space. On this basis, an adaptive filtering noise reduction method based on EWT is proposed. Firstly, the noise signal is decomposed into several sub-bands by EWT, and the empirical wavelet function is used to decompose the mixed signal. The sub-bands corresponding to each noise signal and the mixed signal are independently filtered and de-noised. Finally, the filtering results of each sub-band are accumulated to obtain the final de-noised signal. The effect of parameters on segmentation in EWT is studied experimentally. For periodic deterministic signals, the normalized error is in the order of 10-4 from the reconstruction error point of view, and further reducing the reconstruction error will improve the noise reduction effect. The improvement of the subband signal in the spectrum dynamic range after decomposition is also verified by experiments. Finally, the signal denoising experiment is carried out, and the simulation results show that the proposed method can achieve better convergence performance than direct adaptive filtering, and it is also better for speech signal denoising under the background of pink noise. In order to solve the problem that the noise reduction effect of the non-stationary signal / noise LMS algorithm is limited, another component decomposition method, empirical mode decomposition (EMD) method, is introduced, which breaks away from the Fourier analysis framework, and the envelope of the signal extremum is continuously averaged. Iterative extraction of single component signals can better reflect the local characteristics of the signals and is suitable for the analysis of non-stationary signals. In this paper, two kinds of noise reduction methods proposed by other researchers, which are combined with EMD and adaptive filtering, are studied, and some problems are analyzed. On this basis, the multivariate empirical mode decomposition (MEMD) is proposed to synchronously decompose the mixed signal and the reference noise signal. After the decomposition, the stability of each IMF is enhanced, which is more suitable for processing with adaptive filtering. The effectiveness of the method is verified by noise reduction experiments. In this paper, a new method of noise reduction is proposed, which combines adaptive component decomposition with adaptive filtering. Two different methods of component decomposition are introduced from two angles. The results of noise reduction experiments verify the feasibility of this idea.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

【參考文獻】

相關期刊論文 前10條

1 趙飛翔;劉永祥;霍凱;;基于棧式降噪稀疏自動編碼器的雷達目標識別方法[J];雷達學報;2017年02期

2 王秋生;陳璐;袁海文;劉元慶;劉穎異;;基于經(jīng)驗小波變換的電暈電流降噪方法[J];電網(wǎng)技術;2017年02期

3 孔令杰;;基于小波變換的心電信號降噪處理[J];自動化技術與應用;2014年12期

4 周先春;嵇亞婷;孫文榮;;基于ICA算法的集合經(jīng)驗模態(tài)分解去噪方法[J];軟件;2014年07期

5 李志農(nóng);朱明;褚福磊;肖堯先;;基于經(jīng)驗小波變換的機械故障診斷方法研究[J];儀器儀表學報;2014年11期

6 張袁元;李舜酩;胡伊賢;江星星;郭海東;;LMS方法的改進及聯(lián)合EEMD在振動信號去噪中的應用[J];振動與沖擊;2013年20期

7 王文波;張曉東;汪祥莉;;基于主成分分析的經(jīng)驗模態(tài)分解消噪方法[J];電子學報;2013年07期

8 韓東穎;丁雪娟;時培明;;基于自適應變尺度頻移帶通隨機共振降噪的EMD多頻微弱信號檢測[J];機械工程學報;2013年08期

9 賈海蓉;張雪英;賈麗紅;;改進最大信噪比的獨立成分分析單通道語音增強算法[J];北京理工大學學報;2013年02期

10 余潔;劉利敏;李小娟;趙爭;;利用ICA算法進行全極化SAR影像濾波研究[J];武漢大學學報(信息科學版);2013年02期

相關會議論文 前1條

1 江川;王炳和;陳s，

本文編號：1935044