Vector ARMA estimation: A reliable subspace approach

A parameter estimation method for finite dimensional multivariate linear st ochastic systems, which is guaranteed to produce valid models approximating the true underlying system in a computational time of a polynomial order i n the system dimension, is presented. This is achieved by combining the mai n features of certain stochastic subspace identification techniques with so und matrix Schur restabilizing procedures and multivariate covariance fitti ng, both of which are formulated as linear matrix inequality problems. all aspects of the identification method are discussed, with an emphasis on the two issues mentioned above, and examples of the overall performance are pr ovided for two different systems.