  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.
