A subspace algorithm for certain blind identification problems

The problem of blind identification of p-inputs/q-outputs FIR transfer functions is addressed. Existing subspace identification methods derived for p=1 are first reformulated. In particular, the links between the noise subspace of a certain covariance matrix of the output signals (on which subspace methods build on) and certain rational subspaces associated with the transfer function to be identified are elucidated. Based on these relations, we study the behavior of the subspace method in the case where the order of the transfer function is overestimated. Next, an asymptotic performance analysis of this estimation method is carried out. Consistency and asymptotical normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are presented to demonstrate the potential of the subspace method. Finally, we take advantage of our new reformulation to discuss the extension of the subspace method to the case p>1. We show where the difficulties lie, and we briefly indicate how to solve the corresponding problems. The possible connections with classical approaches for MA model estimations are also outlined