Spectrum estimation in innovation models by a nuclear norm optimization based algorithm

In this paper, identification of multi-input/multi-output (MIMO) state-space models in the innovation form by a regularized-nuclear norm optimization based subspace algorithm is studied. Parametrization issues are carefully addressed for MIMO state-space models. The optimization problem formulated in this paper allows one to utilize a variety of norms in the objective function including the nuclear and the quadratic norms without affecting the parametrization results. A numerical example illustrates the results derived in the paper.

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