New approach to noncausal identification of nonstationary stochastic systems subject to both smooth and abrupt parameter changes

In this paper we consider the problem of finite-interval parameter smoothing for a class of nonstationary linear stochastic systems subject to both smooth and abrupt parameter changes. The proposed parallel estimation scheme combines the estimates yielded by several exponentially weighted basis func...

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Bibliographic Details
Published in2012 IEEE 51st IEEE Conference on Decision and Control (CDC) pp. 889 - 894
Main Authors Niedzwiecki, M., Gackowski, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Algorithm design and analysis
Estimation
Mathematical model
Noise
Smoothing methods
Trajectory
Vectors
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Summary:In this paper we consider the problem of finite-interval parameter smoothing for a class of nonstationary linear stochastic systems subject to both smooth and abrupt parameter changes. The proposed parallel estimation scheme combines the estimates yielded by several exponentially weighted basis function algorithms. The resulting smoother automatically adjusts its smoothing bandwidth to the type and rate of nonstationarity of the identified system. It also allows one to account for the distribution of the measurement noise.
ISBN:9781467320658
146732065X
ISSN:0191-2216
DOI:10.1109/CDC.2012.6427018