Title :
Estimating state-space models in innovations form using the expectation maximisation algorithm
Author :
Wills, Adrian ; Schön, Thomas B. ; Ninness, Brett
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
Abstract :
The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.
Keywords :
expectation-maximisation algorithm; identification; state-space methods; expectation maximisation algorithm; gradient-based search; identification problem; innovation; state-space model; Algorithm design and analysis; Data models; Joints; Kalman filters; Maximum likelihood estimation; Technological innovation;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717145