Identification of an unknown system and recovery of the input signals from observations of the outputs of an unknown multiple-input, multiple-output linear system are considered. Attention is focused on the two-channel case, in which the outputs of a 2*2 linear time invariant system are observed. The approach consists of reconstructing the input signals by assuming that they are statistically uncorrelated and imposing this constraint on the signal estimates. In order to restrict the set of solutions, additional information on the true signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow's (1975) least-squares method for noise cancellation, where the reference signal contains a component of the desired signal. >
[1]
Christian Jutten,et al.
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
,
1991,
Signal Process..
[2]
J. Kiefer,et al.
Stochastic Estimation of the Maximum of a Regression Function
,
1952
.
[3]
Ehud Weinstein,et al.
Maximum likelihood noise cancellation using the EM algorithm
,
1989,
IEEE Trans. Acoust. Speech Signal Process..
[4]
Ehud Weinstein,et al.
Iterative and sequential algorithms for multisensor signal enhancement
,
1994,
IEEE Trans. Signal Process..
[5]
B. Widrow,et al.
Adaptive noise cancelling: Principles and applications
,
1975
.
[6]
Ehud Weinstein,et al.
Criteria for multichannel signal separation
,
1994,
IEEE Trans. Signal Process..