A maximum a posteriori estimate for the source separation problem with statistical knowledge about the mixing matrix
In the blind source separation (BSS) problem, nothing is supposed about the mixing matrix entries. When a prior knowledge about their values is available, the BSS algorithms can be optimized considering this information. We obtain the maximum a posteriori estimate of the source separation problem for prewhitened observed signals and prior statistical knowledge about the mixing matrix for the real, linear, instantaneous case. As new information is included in the formulation of the problem, the variance of classical BSS algorithms can be reduced. (C) 2003 Elsevier B.V. All rights reserved.
maximum a posteriori estimate source separation independent component analysis gram-charlier expansions