CRITERIA FOR MULTICHANNEL SIGNAL SEPARATION

We consider the problem in which we want to separate two (or more) signals that are coupled to each other through an unknown multiple-input-multiple-output linear system (channel). We prove that the signals can be decoupled, or separated, using only the condition that they are statistically independent, and find even weaker sufficient conditions involving their cross-polyspectra. By imposing these. conditions on the reconstructed signals, we obtain a class of criteria for signal separation. These criteria are universal in the sense that they do not require any prior knowledge or information concerning the nature of the source signals. They may be communication signals, or speech signals, or any other 1-D or multidimensional signals (e.g., images). Computationally efficient algorithms for implementing the proposed criteria, that only involve the iterative solution to a linear least squares problem, are presented.