Subspace Algorithms for the Stochastic Identiication Problem 1

Subtitle A new subspace algorithm consistently identiies stochastic state space models directly from given output data, using only semi-innnite block Hankel matrices. Abstract In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-innnite block Hankel matrices. The algorithm is based on the concept of principal angles and directions. We describe how they can be calculated with QR and Quotient Singular Value Decomposition. We also provide an interpretation of the principal directions as states of a non-steady state Kalman lter bank.