【摘要】：盲源分離(Blind Sources Separation,BSS)技術指的是在傳輸信道和源信號都未知的情況下,根據(jù)源信號的統(tǒng)計特性,僅僅利用觀測信號分離出各個源信號的過程。多通道BSS算法已經(jīng)在生物醫(yī)學信號處理、陣列信號處理、移動通信和文本分析與處理等領域得到廣泛應用。近幾年來,單通道盲源分離問題逐漸成為信號處理領域的研究熱點。單通道盲源分離(Single Channel Blind Source Separation,SCBSS)是欠定盲源分離問題的極端情況,它僅僅利用單路觀測信號的特征信息,分離出多路源信號,解決起來十分困難。但是SCBSS又是許多實際系統(tǒng)中常見的問題,因此研究SCBSS算法具有重要的理論意義和應用價值。本文主要研究多通道BSS算法和SCBSS算法。首先,研究了FastICA算法的改進。針對源信號數(shù)較多時,原有的FastICA算法迭代次數(shù)較多和分離性能惡化的問題,提出了Pm-FastICA算法。對算法中的非線性函數(shù)進行Pade逼近,得到能夠減少FastICA算法迭代次數(shù)的有理函數(shù),提高了收斂速度和分離性能。仿真表明,Pm-Fast ICA算法性能優(yōu)于FastICA算法,且隨著源信號數(shù)目的增多,Pm-FastICA算法的性能優(yōu)勢將更明顯。同時提出了一種利用有理多項式非線性函數(shù)的FastICA(簡稱N-FastICA)算法。仿真表明,N-FastICA算法性能優(yōu)于Pm-FastICA算法和FastICA算法,且隨著源信號數(shù)目的增多,N-Fast ICA算法的性能優(yōu)勢將更明顯。其次,研究了基于小波包分解的SCBSS(WPT-ICA)算法。由于小波變換不能很好地表示包含大量細節(jié)信息,基于小波變換的SCBSS算法性能有待提高。對此,本文提出了一種基于小波包分解的SCBSS算法。對觀測信號進行小波包分解,選擇能量百分比較高的系數(shù)進行重構,將重構信號與觀測信號構成多路信號,利用N-FastICA算法實現(xiàn)信號的盲源分離。仿真結果表明,基于小波包分解的SCBSS算法性能優(yōu)于基于小波分解的SCBSS算法。然后,研究了基于經(jīng)驗模態(tài)分解(Empirical Mode Decomposition,EMD)的SCBSS算法。基于EMD的SCBSS算法存在模態(tài)混疊現(xiàn)象,導致分離性能惡化,甚至分離不完全。本文針對該問題,提出了一種基于EMD、主成分分析(Principal Component Analysis,PCA)和獨立分量分析(Independent Component Analysis,ICA)的單通道盲源分離算法(簡稱EP-ICA算法)。該算法利用EMD得到本征模函數(shù)分量(intrinsic mode function,IMF)分量,針對出現(xiàn)模態(tài)混疊的IMF分量,利用信號的周期性構造其多路信號,利用ICA消除模態(tài)混疊,利用PCA和互相關性剔除多路信號中的虛假分量,并將剩余分量信號與觀測信號構成新的多路信號,最后利用N-Fast ICA實現(xiàn)盲源分離。仿真結果表明EP-ICA算法優(yōu)于已有的基于EMD的SCBSS算法。最后,研究了基于變分模式分解(Variational Mode Decomposition,VMD)的SCBSS算法。將VMD引入SCBSS算法中,提出了基于VMD的SCBSS(VMD-SCBSS)算法;同時將反饋機制應用于VMD方法中,提出了一種基于反饋VMD的SCBSS(VMDF-SCBSS)算法。仿真結果表明,VMD-SCBSS算法和VMDF-SCBSS算法的分離性能優(yōu)于EP-ICA算法,VMDF-SCBSS算法具有與VMD-SCBSS算法相當?shù)姆蛛x性能,但該算法無需預知源信號中心頻率差值,能夠自動確定源信號數(shù),算法的運算復雜度低于VMD-SCBSS算法。
[Abstract]:Blind source separation (BSS) technology refers to the process of separating only the individual source signals from the observation signal in the case where both the transmission channel and the source signal are unknown. The multi-channel BSS algorithm has been widely used in the fields of biomedical signal processing, array signal processing, mobile communication and text analysis and processing. In recent years, the problem of single-channel blind source separation has become a hot topic in the field of signal processing. Single-channel blind source separation (SCBSS) is the extreme case of the problem of the separation of blind source. It only uses the characteristic information of single-channel observation signal to separate the multi-channel source signal, which is very difficult to solve. However, the SCBSS is a common problem in many practical systems, so it is of great theoretical and practical value to study the SCBSS algorithm. This paper mainly studies the multi-channel BSS algorithm and the SCBSS algorithm. First, the improvement of the FastICA algorithm is studied. In order to solve the problem of more iteration times and degradation of the original FastICA algorithm, a Pm-FastICA algorithm is proposed. Pade approximation to the non-linear function in the algorithm results in a rational function that can reduce the number of iterations of the FastICA algorithm, and the convergence speed and the separation performance are improved. The simulation results show that the performance of Pm-Fast ICA is better than that of the FastICA algorithm, and the performance advantage of the Pm-FastICA algorithm will be more obvious with the increase of the number of source signals. In this paper, a FastICA (N-FastICA) algorithm using the nonlinear function of rational polynomial is proposed. The simulation shows that the performance of the N-Fast ICA algorithm is better than that of the Pm-FastICA algorithm and the FastICA algorithm, and the performance advantage of the N-Fast ICA algorithm will be more obvious with the increase of the number of source signals. Secondly, we study the SCBSS (WPT-ICA) algorithm based on wavelet packet decomposition. The performance of the SCBSS algorithm based on wavelet transform is to be improved because of the fact that the wavelet transform does not well represent the large amount of detail information. In this paper, an SCBSS algorithm based on wavelet packet decomposition is proposed in this paper. And carrying out wavelet packet decomposition on the observation signal, selecting a coefficient with higher energy percentage to reconstruct, and combining the reconstructed signal and the observation signal to form a multi-channel signal, and utilizing the N-FastICA algorithm to realize the blind source separation of the signal. The simulation results show that the SCBSS algorithm based on wavelet packet decomposition is superior to the SCBSS algorithm based on wavelet decomposition. Then, the SCBSS algorithm based on the empirical mode decomposition (EMD) is studied. The existence of the mode aliasing in the SCBSS algorithm based on EMD leads to the deterioration of the separation performance and even the incomplete separation. In this paper, a single-channel blind source separation algorithm based on EMD, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed. The method uses EMD to obtain an eigenmode function (IMF) component, And the residual component signal and the observation signal form a new multipath signal, and finally, the blind source separation is realized by using the N-Fast ICA. The simulation results show that the EP-ICA algorithm is superior to the existing EMD-based SCBSS algorithm. Finally, the SCBSS algorithm based on the variational mode decomposition (VMD) is studied. In this paper, the VMD-based SCBSS (VMD-SCBSS) algorithm is proposed, and a SCBSS (VMDF-SCBSS) algorithm based on the feedback VMD is proposed. The simulation results show that the separation performance of the VMD-SCBSS algorithm and the VMDF-SCBSS algorithm is better than that of the EP-ICA algorithm, and the VMDF-SCBSS algorithm has the same separation performance as the VMD-SCBSS algorithm, but the algorithm does not need to predict the source signal center frequency difference, can automatically determine the source signal number, and the operation complexity of the algorithm is lower than the VMD-SCBSS algorithm.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

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