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This paper deals with dynamic Blind Source Extraction (BSE) from where themixing parameters characterizing the position of a source of interest (SOI) areallowed to vary over time. We present a new source extraction model calledCvxCSV which is a parameter-reduced modification of the recent ConstantSeparation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as aconvex combination of its initial and final values. We derive a lower bound onthe achievable mean interference-to-signal ratio (ISR) based on theCram\'er-Rao theory. The bound reveals advantageous properties of CvxCSVcompared with CSV and compared with a sequential BSE based on independentcomponent extraction (ICE). In particular, the achievable ISR by CvxCSV islower than that by the previous approaches. Moreover, the model requiressignificantly weaker conditions for identifiability, even when the SOI isGaussian.

Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio