【作者】 王惠刚；

【导师】 李志舜；

【作者基本信息】 西北工业大学 ， 信号与信息处理， 2002， 博士

【摘要】 盲信号处理是一个非常广泛的议题，在许多实际系统中有着应用。本篇论文主要讨论了自适应盲源分离的基本原理和算法，并把源信号具有统计独立同分布的假定扩展到更实际的情形，分别研究了时间相关源的盲分离、非平稳信号的盲分离以及带噪的盲估计，并最终用一个简单的试验来证实其应用。 盲源分离是从观测数据中分离出未知但相互统计独立的信号，混合的过程假定为线性时不变系统。获取盲分离的准则主要从信息论的角度出发，包括信息最大化方法、最小互信息方法和最大似然概率准则，这些准则在一定条件下是等价的。与源的概率密度相关的非线性函数影响着自适应算法的性能，如稳定性、收敛速度和均方误差等。高斯混合模型被用来逼近源的概率密度函数，并导致了一种更鲁棒的盲分离算法。通过讨论卷积混合模型和瞬时混合模型的关系，这些算法也可以扩展到卷积混合模型中，推导了一序列更实用的自适应盲反卷积算法。 时间相关源和非平稳信号的盲分离不仅可以利用源的非高斯性来分离信号，而且还可以利用源信号自身的特性——谱特性和非平稳性来实现分离，因此简单的二阶统计量方法就成为主要的准则。多个矩阵的联合近似对角化(JADE)算法和自适应去相关算法被证明能够获得比较优异的性能。 噪声的存在影响了盲分离算法的性能，通过线性变换不能获得源信号的精确估计。混合参数和源参数的精确估计能有效地重构源信号，一种基于高斯混合模型的盲参数估计自适应算法被证明是有效的。同时也讨论了白噪声下时间相关源和非平稳信号的盲参数估计方法。 最后通过水池试验展示了盲分离算法的应用。更多还原

【Abstract】 Blind signal separation (BSS) is an important topic and has many applications in practices. In this thesis, the principle and algorithm of adaptive blind source separation are discussed, and the assumption that sources are independent identical distributive (i.i.d.) is extended to more practical situations, just like temporal-correlated sources, non-stationary sources and noisy sources. A simple experiment in water tank is executed to testify some algorithms of this thesis."Blind" means that the information of sources and the mixing system is unknown, so the mission of BSS is to recover the original sources from the observed data when the mixing system is assumed Linear Time Invariant (L.T.I.) system. The object criterion is based on information theory, including Information Maximization, Minimize Mutual Information and Maximization Likelihood, all are equivalent. The nonlinear function relative to probability density function (p.d.f.) of sources affects the performance of adaptive algorithm, as stability, convergence speed, mean square error etc. Gaussian Mixture Model (GMM) is provided to approximate the p.d.f. of sources and leads to a robust adaptive algorithm. By the relationship of the instantaneous mixture model with the convolutive mixture model, some BSS algorithms can be extended to blind deconvolution, which are more practical adaptive algorithms.For temporal-correlated sources and non-stationary sources, the characteristic of sources -- different spectrum or non-stationary, is used to separate signals, so second order statistic is enough. Joint Approximate Diagonalization (JADE) method and adaptive decorrelation have better performance than other algorithms.Noise reduces the algorithm performance and prevent linear transform from getting the exact original sources. The accurate estimation for mixing parameters and source parameter can reconstruct sources in least-square meaning, which can be solved by an adaptive blind estimation algorithm based on GMM. How to deal with noise for temporal-correlated sources and non-stationary sources also is discussed.Finally, data from a simple experimental in water tank is used to analyze the above algorithms.更多还原