Title :
Maximum likelihood identification of stochastic linear systems
Author_Institution :
Purdue University, Lafayette, IN, USA
fDate :
2/1/1970 12:00:00 AM
Abstract :
The maximum likelihood estimation of the coefficients of multiple output linear dynamical systems and the noise correlations from the noisy measurements of input and output are discussed. Conditions are derived under which the estimates converge to their true values as the number of measurements tend to infinity. The computational methods are illustrated by several numerical examples.
Keywords :
Linear systems, stochastic discrete-time; Parameter estimation; maximum-likelihood (ML) estimation; Information analysis; Information theory; Instruments; Linear systems; Maximum likelihood detection; Maximum likelihood estimation; Noise measurement; Predictive models; Stochastic resonance; Stochastic systems;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1970.1099344
