本文選題：信號(hào)去噪 + 濾波器　； 參考：《揚(yáng)州大學(xué)》2014年碩士論文

【摘要】：在信號(hào)采集、傳輸和處理過程中,不可避免會(huì)含有噪聲,直接影響后續(xù)處理的結(jié)果,因此如何對(duì)信號(hào)進(jìn)行去噪處理,是目前信號(hào)處理領(lǐng)域中的國內(nèi)外研究熱點(diǎn)問題之一。 目前關(guān)于信號(hào)去噪的方法很多,常用的去噪方法有基于傅立葉變換的信號(hào)去噪,由于傅立葉變換的時(shí)頻單一性,信號(hào)的去噪效果較差。小波變換具有時(shí)域局部化特征、多分辨率特性、解相關(guān)特性和選基靈活性等特征,已廣泛應(yīng)用于雷達(dá)信號(hào)處理、語音識(shí)別、數(shù)據(jù)壓縮、信號(hào)處理、模式識(shí)別、信號(hào)去噪等領(lǐng)域。針對(duì)不同的含噪信號(hào),對(duì)七種常規(guī)小波基的去噪性能進(jìn)行分析與實(shí)驗(yàn)比較,構(gòu)造出基于結(jié)構(gòu)化的9/7小波濾波器組和7/13小波濾波器組,并對(duì)其濾波器組進(jìn)行性能分析比較。 不同的小波基具有不同的時(shí)頻特性,選擇的小波基不同,對(duì)應(yīng)的去噪的效果也不相同,所以在去噪過程中,小波基的選擇,會(huì)直接影響小波去噪的效果,如何根據(jù)信號(hào)的特點(diǎn)選擇最佳小波基,是目前國內(nèi)外學(xué)者的研究的關(guān)鍵問題。針對(duì)現(xiàn)有小波基的不足,提出一種基于信號(hào)匹配的最優(yōu)小波去噪方法。該方法根據(jù)信號(hào)在尺度空間的最大投影而構(gòu)造的能量匹配準(zhǔn)則,利用結(jié)構(gòu)化小波濾波器組,結(jié)合遺傳算法,構(gòu)造出與信號(hào)能量一致的最優(yōu)能量匹配小波。并根據(jù)波形匹配準(zhǔn)則,結(jié)合結(jié)構(gòu)化小波濾波器組,利用優(yōu)化函數(shù)構(gòu)造出與信號(hào)波形一致的最優(yōu)波形匹配小波。實(shí)驗(yàn)確定結(jié)果表明,基于信號(hào)匹配的最優(yōu)小波去噪方法的去噪效果優(yōu)于其它小波。 針對(duì)不同的信號(hào)去噪,除對(duì)不同小波基研究與最優(yōu)小波基選取外,還要對(duì)基于小波分解層數(shù)的信號(hào)去噪進(jìn)行了研究,發(fā)現(xiàn)小波分解層數(shù)與含噪信號(hào)的受污染程度存在一定關(guān)系。在實(shí)際的信號(hào)小波去噪過程中,不同的含噪信號(hào),其小波分解層數(shù)不固定,且不同分解層數(shù)會(huì)對(duì)去噪效果產(chǎn)生很大的影響,針對(duì)這一問題,提出一種基于最優(yōu)分解層去噪方法,該方法利用各個(gè)小波分解層的能量關(guān)系,即信噪比,結(jié)合優(yōu)化算法來確定最優(yōu)分解層進(jìn)行去噪。實(shí)驗(yàn)確定結(jié)果表明,在它的最優(yōu)分解層上的消噪效果達(dá)到了最佳。
[Abstract]:In the process of signal acquisition, transmission and processing, it is inevitable that there will be noise, which directly affects the results of subsequent processing. Therefore, how to Denoise the signal is one of the hot issues in the field of signal processing at home and abroad. At present, there are many methods of signal de-noising. The common methods of de-noising are based on Fourier transform. Because of the singularity of time-frequency of Fourier transform, the effect of signal de-noising is poor. Wavelet transform has been widely used in radar signal processing, speech recognition, data compression, signal processing, pattern recognition, signal denoising and so on. For different noisy signals, the performance of seven kinds of conventional wavelet bases is analyzed and compared by experiments. A structured 9 / 7 wavelet filter bank and a 7 / 13 wavelet filter bank are constructed, and the performance of the filter banks is analyzed and compared. Different wavelet bases have different time-frequency characteristics. Different wavelet bases have different corresponding denoising effects, so the selection of wavelet bases will directly affect the effect of wavelet denoising in the process of de-noising. How to select the best wavelet basis according to the characteristics of signal is the key problem of scholars at home and abroad. An optimal wavelet denoising method based on signal matching is proposed to overcome the shortcomings of existing wavelet bases. Based on the energy matching criterion constructed by the maximum projection of the signal in the scale space, the optimal energy matching wavelet which is consistent with the signal energy is constructed by using the structured wavelet filter bank and the genetic algorithm. According to the waveform matching criterion and the structured wavelet filter bank, the optimal waveform matching wavelet is constructed by using the optimization function. The experimental results show that the denoising effect of the optimal wavelet denoising method based on signal matching is better than that of other wavelets. For different signal denoising, besides the study of different wavelet bases and the selection of optimal wavelet bases, the signal denoising based on wavelet decomposition layers is also studied. It is found that the number of wavelet decomposition layers is related to the degree of contamination of noisy signals. In the actual signal wavelet denoising process, the wavelet decomposition layer number of different noisy signal is not fixed, and the different decomposition layer number will have a great influence on the de-noising effect. In view of this problem, a denoising method based on the optimal decomposition layer is proposed. In this method, the energy relationship of each wavelet decomposition layer, i.e. SNR, is used to determine the optimal decomposition layer for denoising. The experimental results show that the denoising effect on the optimal decomposition layer is the best.
【學(xué)位授予單位】：揚(yáng)州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.4

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