N4SID - SUBSPACE ALGORITHMS FOR THE IDENTIFICATION OF COMBINED DETERMINISTIC STOCHASTIC-SYSTEMS

Recently a great deal of attention has been given to numerical algorit hms for subspace state space system identification (N4SID). In this pa per, we derive two new N4SID algorithms to identify mixed deterministi c-stochastic systems. Both algorithms determine state sequences throug h the projection of input and output data. These state sequences are s hown to be outputs of non-steady state Kalman filter banks. From these it is easy to determine the state space system matrices. The N4SID al gorithms are always convergent (non-iterative) and numerically stable since they only make use of QR and Singular Value Decompositions. Both N4SID algorithms are similar, but the second one trades off accuracy for simplicity. These new algorithms are compared with existing subspa ce algorithms in theory and in practice.