本文關鍵詞： 盲分離 獨立成分分析 負熵 峭度 快速固定點算法　出處：《上海交通大學》2015年碩士論文　論文類型：學位論文

【摘要】：目前信號處理領域中最熱點的研究問題之一就是盲源分離問題(BBS),它的主要研究工作就是,在對系統(tǒng)的源信號和混合系統(tǒng)都未知的情況下,處理一組以時間序列或者并行信號的形式表示的觀測變量,最終分離出想要求解的源信號。盲源分離的典型例子有很多:手機中的射頻干擾信號、傳感器記錄的腦電波以及麥克風錄取的混合語音信號等。而處理這類問題最有效的方法就是獨立成分分析(ICA),它是隨著盲分離問題研究的不斷發(fā)展而引起廣泛關注的。ICA方法的基本思想是,首先通過傳感器采集到觀測信號,再根據(jù)信號的統(tǒng)計特性尋找一個合適的目標函數(shù),通過選取迭代算法對目標函數(shù)優(yōu)化,得到最優(yōu)的解混信道,將觀測信號通過解混信道的處理就可以得到想要求解的源信號的估計�；诿ぴ捶蛛x問題的研究背景下,本文簡述了獨立成分分析的研究意義和發(fā)展歷史,通過對ICA基本原理的研究分析得到基本模型存在的不確定性以及約束條件:研究對象必須是非高斯信號,并且滿足相互獨立的統(tǒng)計特性。ICA問題可以簡化為通過一定的優(yōu)化算法得到選定的目標函數(shù)的最優(yōu)解,從而分離出待求解的源信號,由此從理論上重點介紹了獨立成分分析的幾種典型的目標函數(shù)和優(yōu)化算法。在ICA模型估計之前,必須要對測量信號作預處理,包括中心化和白化這兩個處理過程,本文從理論上說明了預處理可以有效地減少ICA中需要預估模型參數(shù)的數(shù)目。快速固定點算法(FastICA)是獨立成分分析中最常用的一種快速算法,根據(jù)非高斯性的評價指標的不同,本文介紹了兩種FastICA算法:基于峭度的FastICA算法和基于負熵的FastICA算法。本課題的核心研究工作在于,通過對兩種原有算法的原理分析,針對其存在的問題,分別提出了相應的改進方案。對于基于峭度的FastICA算法存在的收斂不穩(wěn)定問題,本文提出了通過共軛梯度法對原算法進行改進,實驗結果表明,改進后的算法不僅分離效果更佳,而且改善了原算法收斂不穩(wěn)定的問題。對于基于負熵的FastICA算法,本文分別提出了通過最速下降法以克服原算法易受初始值影響的缺點,用差商法代替求導以降低了原算法的復雜性。仿真實驗結果表明,改進后的算法不僅分離出的信號更逼近源信號,而且解決了初始值的問題,算法速度也變得更快。最后,將本文中提出的兩種改進的FastICA算法分別應用于分離人工混合和實際混合的語音信號,對分離結果進行分析得出,基于峭度的改進算法的分離效果更佳,而基于負熵的改進算法的收斂速度更快,但由于兩種改進算法的分離效果差距并不大,基于負熵的改進算法以其收斂速度的優(yōu)勢更具有實用性。
[Abstract]:At present, one of the hottest research problems in the field of signal processing is the blind source separation (BSS) problem. Its main research work is that the source signal and hybrid system are unknown. Processing a set of observation variables in the form of a time series or a parallel signal, and finally separating the source signal to be solved. There are many typical examples of blind source separation: radio frequency interference signals in mobile phones, Brain waves recorded by sensors and mixed speech signals recorded by microphones, etc. The most effective way to deal with this kind of problems is independent component analysis (ICA), which has attracted wide attention with the development of blind separation research. The basic idea of the ICA approach is, First, the observed signals are collected by the sensor, then a suitable objective function is found according to the statistical characteristics of the signal, and the optimal unmixing channel is obtained by selecting the iterative algorithm to optimize the objective function. The estimation of the source signal to be solved can be obtained by processing the observed signal through the de-mixing channel. Based on the research background of blind source separation problem, the research significance and development history of independent component analysis (ICA) are briefly described in this paper. Based on the analysis of the basic principle of ICA, the uncertainty and constraint conditions of the basic model are obtained: the object of study must be non-#china_person0# signal, The ICA problem can be simplified to obtain the optimal solution of the selected objective function by a certain optimization algorithm, and the source signal to be solved can be separated. Therefore, several typical objective functions and optimization algorithms of independent component analysis (ICA) are introduced in theory. Before ICA model estimation, the measurement signal must be preprocessed, including centralization and whitening. In this paper, it is theoretically explained that preprocessing can effectively reduce the number of estimated model parameters in ICA. Fast fixed point algorithm (FastICA) is one of the most commonly used fast algorithms in independent component analysis (ICA), according to the difference of non-#china_person0# evaluation indexes. This paper introduces two kinds of FastICA algorithms: FastICA algorithm based on kurtosis and FastICA algorithm based on negative entropy. For the unsteady convergence of FastICA algorithm based on kurtosis, the conjugate gradient method is proposed to improve the original algorithm. The experimental results show that the improved algorithm not only has better separation effect. Moreover, the unsteady convergence of the original algorithm is improved. For the FastICA algorithm based on negative entropy, this paper proposes to overcome the shortcoming that the original algorithm is vulnerable to the influence of initial value by the steepest descent method. The difference quotient method is used instead of the derivative method to reduce the complexity of the original algorithm. The simulation results show that the improved algorithm not only approximates the source signal, but also solves the problem of initial value, and the speed of the algorithm becomes faster. The two improved FastICA algorithms proposed in this paper are applied to the separation of speech signals with manual mixing and actual mixing respectively. The analysis of the separation results shows that the improved algorithm based on kurtosis has better separation effect. The improved algorithm based on negative entropy has a faster convergence speed, but because the separation effect of the two improved algorithms is not big, the improved algorithm based on negative entropy is more practical because of its advantage of convergence speed.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN912.3

【共引文獻】

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1 易長平;趙明生;崔正榮;;基于獨立分量分析的爆破振動信號分離仿真試驗[J];爆破;2010年01期

2 趙明生;張建華;易長平;;獨立分量分析在爆破振動信號分離中的應用初探[J];爆炸與沖擊;2011年02期

3 劉洪林;李海山;;ICA及其在氣液兩相流辨識中的應用[J];吉林大學學報(地球科學版);2009年01期

4 陳超;高憲軍;李德鑫;;基于獨立分量分析的混疊跳頻信號分離算法[J];吉林大學學報(信息科學版);2008年04期

5 陳永彬;;TDMA信號態(tài)勢信息獲取技術[J];成都電子機械高等�？茖W校學報;2009年01期

6 閆彩虹;曾孝平;;基于ICA的胎兒心電信號提取算法的比較[J];重慶工學院學報(自然科學版);2009年10期

7 金驥;魯華祥;;核ICA在電流傳感器相位差監(jiān)測中的應用[J];傳感器與微系統(tǒng);2008年12期

8 賈輝;林義剛;李娜;李宏;;獨立分量分析在圖像去噪中的應用[J];長江大學學報(自然科學版)理工卷;2010年02期

9 徐彬;芮國勝;陳必然;;一種基于頻移濾波器的混合信號盲恢復算法[J];電訊技術;2011年11期

10 石樂賢;李燕青;王洋;葛Z，

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