Paper Info
Title
A Comparison of Two Methods for State Estimation: A Statistical Kalman Filter, and a Deterministic Interval-Based Approach
Abstract
In an uncertain framework the performance of two methods of state estimation for discrete-time linear systems are compared on a pedagogical example. The first one is the well known Kalman filter, which is accurate when the measurement noises and the state disturbances are assumed Gaussian white noises and their statistical properties are available. The second one is a set-membership state estimator, which is also based on the prediction-correction principle. Based on the observability assumption of linear systems combined with interval analysis, both stages of this estimator are carried out in a guaranteed and efficient way. In this study, the performance of both state estimation algorithms are evaluated under two scenarios. In the first scenario, the state disturbances and measurement noise are considered Gaussian and in the second scenario these signals are considered unknown-but-bounded with known bounds.
Year
DOI
Venue
2018
10.1109/MED.2018.8442525
2018 26th Mediterranean Conference on Control and Automation (MED)
Keywords
Field
DocType
set-membership state estimator,prediction-correction principle,interval analysis,state estimation algorithms,state disturbances,statistical Kalman filter,deterministic interval-based approach,uncertain framework,discrete-time linear systems,Gaussian white noises,statistical properties,observability assumption,measurement noises
Observability,State estimator,Linear system,Control theory,Computer science,Kalman filter,Gaussian,Interval arithmetic,Estimator
Conference
ISSN
ISBN
Citations 
2325-369X
978-1-5386-7499-4
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Nacim Meslem1547.97
Nacim Ramdani214821.23