基于混合混合向量的动态独立分量提取：平均干扰信号比下限
Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio
摘要：
本文讨论了动态盲源提取（BSE），其中混合参数表征的感兴趣的源（SOI）的位置允许随时间变化。我们提出了一个新的源提取模型CvxCSV，这是一个参数减少修改最近的常数分离矢量（CSV）混合模型。在CvxCSV中，混合向量作为其初始值和最终值的凸组合而演化。基于Cramér-Rao理论，推导了可达平均干扰信号比（ISR）的下界。该绑定揭示了CvxCSV与CSV相比，并与基于独立分量提取（ICE）的顺序BSE相比，具有优越的性能。特别是，可实现的ISR CvxCSV是低于以前的方法。此外，该模型需要显着较弱的可识别性的条件，即使当SOI是高斯。
英文摘要：
This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian.
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