Least-Mean-Square Receding Horizon Estimation
SCIE
SCOPUS
- Title
- Least-Mean-Square Receding Horizon Estimation
- Authors
- Bokyu Kwon; Han, S
- Date Issued
- 2012-03
- Publisher
- MDPI
- Abstract
- We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation. The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon. Any a priori state information is not required, and existing artificial constraints for easy derivation are not imposed. For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form. For numerical reliability, the iterative form is presented with forward and backward computations. It is shown through a numerical example that the proposed LMS RH estimator has better robust performance than conventional Kalman estimators when uncertainties exist.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/11362
- DOI
- 10.1155/2012/631759
- ISSN
- 1024-123X
- Article Type
- Article
- Citation
- MATHEMATICAL PROBLEMS IN ENGINEERING, vol. 2012, 2012-03
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