Abstract
In this paper, a geometrically inspired algorithm is derived for identification of state space models for multivariable linear time-invariant systems using noisy input-output measurements. The algorithm contains two conceptual steps which allow a robust implementation using SVD techniques: 1. Using canonical correlation, first a state vector sequence is realized. 2. The system matrices are then obtained at once from the least squares solution of a set of linear equations.
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