SUBSPACE ALGORITHMS FOR THE STOCHASTIC IDENTIFICATION PROBLEM

In this paper, we derive a new subspace algorithm to consistently iden tify stochastic state space models from given output data without form ing the covariance matrix and using only semi-infinite block Hankel ma trices. The algorithm is based on the concept of principal angles and directions. We describe how they can be calculated with QR and Quotien t Singular Value Decomposition. We also provide an interpretation of t he principal directions as states of a non-steady state Kalman filter bank.