An efficient frequency domain state-space identification algorithm: robustness and stochastic analysis

In this paper we present and analyze a novel algorithm for identifying linear time-invariant discrete time state-space models from frequency response data. The algorithm is noniterative and exactly recovers a true system of order n, if n+2 noise-free uniformly spaced frequency response measurements are given. Analysis show that if the measurements are perturbed with errors upper bounded by ε the identification error will be upper bounded by ε and hence the algorithm is robust. An asymptotic stochastic analysis show, under weak assumptions, that the algorithm is consistent if the measurements are contaminated with noise