Title :
Least-squares estimation of linear systems in the presence of noise
Author :
Gibson, J.S. ; Lee, G.H.
Author_Institution :
Dept. of Mech. Aerosp. & Nucl. Eng., California Univ., Los Angeles, CA, USA
Abstract :
This paper addresses the problem of fitting digital input/output models to data generated by linear systems in the presence of white process and sensor noise. The systems of interest have state-space realizations in Hilbert spaces. Both finite-dimensional and infinite-dimensional input/output models are considered. The main results characterize the asymptotic values to which least-squares parameter estimates converge with increasing amounts of data
Keywords :
least squares approximations; linear systems; parameter estimation; state-space methods; white noise; Hilbert spaces; asymptotic values; digital input/output models; finite-dimensional input/output models; infinite-dimensional input/output models; least-squares estimation; least-squares parameter estimates; linear systems; sensor noise; state-space realizations; white process noise; Band pass filters; Ear; Hilbert space; Kalman filters; Linear systems; Parameter estimation; Predictive models; State estimation; Statistics; Steady-state;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325451
