Abstract
A multidimensional adaptive algorithm with a forward/backward bootstrapped structure for a dispersive channel environment is proposed. It is an alternative multisignal separator where the loop-bandwidth of the signal separator structure and steady-state performance are crucial. It separates superimposed convolutive multiuncorrelated signals. The bootstrapped adaptive algorithm, which does not require a training sequence, employs the minimization of output signal correlations as optimization criteria. The control algorithm is set for the multidimensional case. The learning process of the 2-D signal separator using computer simulation is investigated and compared with that of the least mean square (LMS) algorithm for different cross-channel eigenvalue spreads.

This publication has 1 reference indexed in Scilit: