DocumentCode
2567460
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
fYear
2010
fDate
15-17 Dec. 2010
Firstpage
5524
Lastpage
5529
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location
Atlanta, GA
ISSN
0743-1546
Print_ISBN
978-1-4244-7745-6
Type
conf
DOI
10.1109/CDC.2010.5717145
Filename
5717145
Link To Document