中文摘要：

作为盲信号处理的独立成分分析方法的扩展,独立子空间分析具有更广阔的应用前景.本文首先给出了独立子空间分析的一般定义和正则化定义,同时把其与独立成分分析方法进行了对比.此外,讨论了独立子空间分析的可分离性与解的唯一性问题.基于极大似然估计和自然梯度方法,本文给出了独立子空间分析的自然梯度算法.仿真实验通过二维的独立子空间分析说明本文提出算法的有效性.

英文摘要：

Standard blind signal separation（BSS） model and methods have been successfully applied to many areas of science.The basic model assumes that the observed signals are linear superpositions of underlying hidden source signals.Most of the blind signal separation algorithms are based on the independent assumption of the source signals,and are called independent component analysis（ICA）.However,the independence property of sources may not hold in some real-world situations,especially in biomedical signal processing and image processing,and therefore the standard independent component analysis cannot give the expected results.Some techniques have been developed in recent years that relax the assumptions of basic independent component analysis model and generalize the independent component analysis problem.Among many extensions of the basic independent component analysis model,several researchers have studied the case where the source signals are not statistically independent.Related models are generally recognized as dependent component analysis（DCA） model.As an extended independent component analysis method for blind signal separation,independent subspace analysis has more applications than the independent component analysis.The general and detailed definition of the independent subspace analysis（ISA） model is given at first and the relationship between independent component analysis and independent subspace analysis methods is also discussed.Moreover,the separateness and uniqueness of the independent subspace analysis model is discussed.Based on the maximum likelihood theory and natural gradient method,the natural gradient separation algorithm for independent subspace analysis model is constructed.Simulation result shows that the proposed algorithm is able to separate the independent subspace analysis mixed source signals.