A novel extension of Independent Component and Independent Vector Analysis for blind extraction/separation of one or several sources from time-varying mixtures is proposed. The mixtures are assumed to be separable source-by-source in series or in parallel based on a recently proposed mixing model that allows for the movements of the desired source while the separating beamformer is time-invariant. The popular FastICA algorithm is extended for these mixtures in one-unit, symmetric and block-deflation variants. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided; it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals.

中文翻译：

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动态独立分量/矢量分析：时变波束形成器可分离的时变线性混合

提出了一种新的独立分量和独立矢量分析扩展，用于从时变混合物中盲提取/分离一种或几种来源。根据最近提出的混合模型，假设混合物是串联或并联的可分离源，该混合模型允许所需源的运动，而分离波束形成器是时不变的。流行的FastICA算法以一种单位，对称和块放气变量的形式针对这些混合物进行了扩展。这些算法是在统一框架内派生的，因此它们既可以应用于实值域也可以应用于复数值域，并且与独立矢量分析类似，可以联合应用于多种混合体。提供了单机算法的性能分析；它显示了在给定的混合和统计模型下的渐近效率。数值模拟证实了分析的有效性，证实了算法在分离运动源中的有用性，并显示出较高的收敛速度和分离超高斯和亚高斯信号的能力。