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Optimal and suboptimal algorithms for identification of time-varying systems with randomly drifting parameters
Abstract
Noncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothingis based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman filter-based parameter trackers can be obtained by means of backward-time filtering of the estimates yielded by both algorithms.When system parameters drift according to the random walk model and the adaptation gain is sufficiently small, the properly tuned two-stage Kalman filtering/smoothing algorithm, derived in the paper, achieves the Cram´er-Rao type lower smoothing bound, i.e. it is the optimal noncausal estimation scheme. Under the same circumstances performance of the modified exponentially weighted least-squares algorithm is often only slightly inferior to that of the Kalman filter-based smoother.
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie z listy filadelfijskiej
- Published in:
-
AUTOMATICA
no. 44,
pages 1718 - 1727,
ISSN: 0005-1098 - Language:
- English
- Publication year:
- 2008
- Bibliographic description:
- Niedźwiecki M.: Optimal and suboptimal algorithms for identification of time-varying systems with randomly drifting parameters// AUTOMATICA. -Vol. 44., nr. nr 7 (2008), s.1718-1727
- Verified by:
- Gdańsk University of Technology
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