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 Cram\'er-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.
翻译:本文涉及动态的盲人源采掘(BSE), 其中允许不同利益源位置的混合参数(SOI) 随时间变化。 我们提出了一个名为 CvxCSV 的新源提取模型, 称为 CvxCSV, 这是最近对常数分离矢量混合模型的参数减少的修改。 在 CvxCSV 中, 混合矢量是其初始值和最终值的组合。 我们从基于 Cram\'er- Rao 理论的可实现的平均干扰- 信号比率( ISR) 中得出了一个较低的界限。 约束显示, CvxCSV 与 CSV 相比具有优势性, 并且与基于独立部件提取的顺序 BSE (ICE) 比较。 特别是, CvxCSV 可实现的ISR 低于先前方法。 此外, 该模型要求识别性条件要低得多, 即使SOI 是高斯。