FAST SUBSPACE-BASED SYSTEM-IDENTIFICATION - AN INSTRUMENTAL VARIABLE APPROACH

Recently developed subspace-based system identification (4SID) techniq ues have opened new routes to the identification of multi-input multi- output systems. The 4SID techniques guarantee convergence, and run fas ter than the statistically efficient prediction error methods without much performance loss. The resulting computational load of the 4SID te chniques is O(NM(2)), where N is the data length and M is the sliding window size. However, the computational burden O(NM(2)) can become pro hibitively large as N and M grow large. Noting that the major bottlene ck comes from the QR factorization of an M X N data matrix and that th e existing 4SID techniques do not exploit the structure of the matrice s arising in the identification procedure, we propose a new implementa tion of the existing 4SID, which reduces the computational burden to O (NM) by exploiting the displacement and low-rank structure of the matr ices.