Vector ARMA estimation: an enhanced subspace approach

A parameter estimation method for finite-dimensional multivariate linear stochastic systems is presented which is guaranteed to produce valid models close enough to the true underlying model, in a computational time of at most a polynomial order of the system dimension. This is achieved by combining the main features of certain stochastic subspace identification techniques together with sound statistical order estimation methods, matrix Schur restabilization procedures and multivariate covariance fitting, the latter formulated as linear matrix inequality problems. In this paper we make emphasis on the last issues mentioned, and provide an example of the overall performance for a multivariable case