State filtering and parameter estimation for state space systems with scarce measurements

This paper considers the state filtering and parameter estimation problems for state space systems with scarce output availability. When the scarce states are available, a least squares based algorithm and an observer based parameter estimation algorithm are developed to estimate the system paramete...

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Veröffentlicht in:Signal processing Jg. 104; S. 369 - 380
1. Verfasser: Ding, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.2014
Elsevier
Schlagworte:
Algorithms
Applied sciences
Availability
Detection, estimation, filtering, equalization, prediction
Estimates
Exact sciences and technology
Filtering
Filtration
Information, signal and communications theory
Least squares method
Missing data
Observers
Parameter estimation
Recursive identification
Signal and communications theory
Signal processing
Signal, noise
State filtering
Telecommunications and information theory
Signal processing
Missing data
Recursive identification
Parameter estimation
State filtering
Availability
Performance evaluation
Filtering
State space
State space method
Algorithm
Least squares method
Observer
Recursive method
ISSN:0165-1684, 1872-7557
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Beschreibung
Zusammenfassung:This paper considers the state filtering and parameter estimation problems for state space systems with scarce output availability. When the scarce states are available, a least squares based algorithm and an observer based parameter estimation algorithm are developed to estimate the system parameter matrices and states. For the case with unknown states, a combined parameter estimation and state filtering algorithm is presented for canonical state space models, using the reconstructed states for the parameter estimation. Finally, an example is provided to test the effectiveness of the proposed algorithms. •State and parameter estimation problems are studied for state space systems.•A least squares based and an observer based estimation algorithms are developed.•A combined parameter estimation and state filtering algorithm is presented.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2014.03.031