论文标题
与混合载体混合载体的动态独立组件提取：平均干扰与信号比的下限
Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio
论文作者
论文摘要
本文介绍了动态盲源提取（BSE），其中从表征感兴趣的位置的混合参数（SOI）可以随时间变化而变化。我们提出了一种称为CVXCSV的新源提取模型，该模型是对最近常数分离矢量（CSV）混合模型的参数还原修改。在CVXCSV中，混合向量作为其初始和最终值的凸组合演变。我们根据Cramér-Rao理论得出了可实现的平均干扰 - 信号比率（ISR）的下限。与CSV相比，该结合揭示了CVXCSV的优势性能，并与基于独立组件提取（ICE）的顺序BSE进行了比较。特别是，CVXCSV可实现的ISR低于先前方法。此外，即使SOI是高斯，该模型也需要明显较弱的可识别性条件。
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.
