On blind MIMO channel estimation and blind signal separation in unknown additive noise

The problem of estimating the impulse response function of an FIR multiple-input multiple-output (MIMO) system given only the noisy measurements of the vector output of the system, is considered. The system is assumed to be driven by a spatially and temporally i.i.d. non-Gaussian vector sequence (which is not observed). The problem of blind separation of independent linear signals from their convolutive mixtures also leads to the above mathematical model. The model order is unknown. The FIR N/spl times/M (N/spl ges/M) MIMO transfer function is assumed to have full column rank on the unit circle; there are no other assumptions. Higher-order cumulant matching is used to consistently estimate the MIMO impulse response via nonlinear optimization. For blind signal separation the estimated channel is used to decompose the received signal at each sensor into its independent signal components via a Wiener filter. A recently proposed inverse filter criteria based approach (which yields biased estimates in noise) is used to obtain initialization for the cumulant matching approach. A simulation example is presented to illustrate the two approaches for both channel estimation as well as convolutive signal separation.