Asymptotic parameter variance analysis for BlindSignal Separation

Some aspects of the source separation problem is studied in this paper. Unmeasurable source signals are mixed by means of a channel system resulting in measurable output signals. These output signals can be used to determine a separation structure in order to extract the sources. When solving the source separation problem the channel lter parameters have to be estimated. This paper presents a compact and computationally appealing formula for computing a lower bound for the variance of these parameters, in a general Many Inputs Many Outputs scenario. This lower bound is the asymptotic (assuming the number of data samples to be large) Cram er-Rao lower bound. Furthermore, the CRLB is analyzed and computed for a Two Inputs Two Outputs system and compared with the results from two separation algorithms: a Prediction Error method and a criterion based correlation method.