Subspace algorithms for the stochastic identification problem

The authors derive a novel algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices. The algorithm is based on the concept of principle angles and directions. The authors describe how these can be calculated with only QR and QSVD decompositions. They also provide an interpretation of the principle directions as states of a non-steady-state Kalman filter. With a couple of examples, it is shown that the proposed algorithm is superior to the classical canonical correlation algorithms