论文标题
动态独立组件/矢量分析：时间变化的线性混合物可通过时间不变的光束器分离
Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers
论文作者
论文摘要
提出了独立成分和独立矢量分析的新型扩展，以从时变混合物中盲目提取/分离一个或几个来源。基于最近提出的混合模型，假定混合物以串联或并联为单位的源，该模型允许所需源的运动，而分离的光束形式是时间不变的。这些混合物在单个单位，对称和嵌入式变体中扩展了流行的FastICA算法。该算法是在统一框架内得出的，因此它们适用于实用值和复杂值域中，并共同用于几种混合物，类似于独立的向量分析。提供了单位算法的性能分析；它显示了其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，确认了算法在移动源分离中的有用性，并显示了分离超级高斯和高斯信号的融合速度和能力的较高速度。
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.
