Asymptotic Variance Expressions for a Frequency Domain Subspace based System Identification Algorithm
1995 (English)In: Proceedings of the 34th IEEE Conference on Decision and Control, 1995, 1234-1239 vol.2 p.Conference paper (Refereed)
A frequency domain identification algorithm is analyzed. The algorithm identifies state-space models given samples of the frequency response function given at equidistant frequencies. A first order perturbation analysis is performed revealing an explicit expression of the resulting transfer function perturbation. Stochastic analysis show that the estimate is asymptotically (in data) normal distributed and an expression of the resulting variance is derived. Monte Carlo simulations illustrates the validity of the derived variance also for the nonasymptotic case and a comparison with the Cramer-Rao lower bound shows that the algorithm is suboptimal.
Place, publisher, year, edition, pages
1995. 1234-1239 vol.2 p.
Identification, Subspace method, Stochastic analysis, Variance expression
IdentifiersURN: urn:nbn:se:liu:diva-93736DOI: 10.1109/CDC.1995.480266ISBN: 0-7803-2685-7OAI: oai:DiVA.org:liu-93736DiVA: diva2:628982
34th IEEE Conference on Decision and Control, New Orleans, LA, USA, December, 1995
FunderSwedish Research Council