A recursive algorithm for linear system identification

This paper deals with the pole-zero identification of a linear system from a measured input-output record. It is shown that the minimization of a modified version of the squared Kalman equation error can be implemented by an order recursive algorithm in the time domain. The algorithm is based on the Gram-Schmidt orthogonalization of intertwined Krylov sequences involving a skew self-adjoint linear operator.