Autoregressive independent process analysis with missing observations

The goal of this paper is to search for independent multidimensional processes subject to missing and mixed observations. The corresponding cocktail-party problem has a number of successful applications, however, the case of missing observations has been worked out only for the simplest Independent Component Analysis (ICA) task, where the hidden processes (i) are one-dimensional, and (ii) signal generation is independent and identically distributed (i.i.d.) in time. Here, the missing observation situation is extended to processes with (i) autoregressive (AR) dynamics and (ii) multidimensional driving sources. Performance of the solution method is illustrated by numerical examples.