NDLI: Window length selection in linear receding horizon filtering

Content Provider	IEEE Xplore Digital Library
Author	Ju Hong Yoon Du Yong Kim Shin, V.
Copyright Year	2008
Description	Author affiliation: Dept. of Mechatron., Gwangju Inst. of Sci. & Technol., Gwangju (Ju Hong Yoon; Du Yong Kim; Shin, V.)
Abstract	A receding horizon filtering problem for linear systems with norm-bounded time-varying uncertainties is considered. The main goal of this paper is to choose the reasonable window length (WL) which enables us to adjust the modeling uncertainty considering not only computational cost but also the accuracy. The decision of the WL is an important step for receding horizon filter designing. This paper presents a novel algorithm for decision of optimal WL. Two methods are proposed. The first algorithm decides the optimal WL by considering lower bound and upper bound of the uncertainty. Secondly hybrid approach which is the combination of the Kalman filter and the optimal receding horizon filter for suitable situations respectively. The performance of the receding horizon filter with proposed WL is illustrated and compared to other finite memory filters.
Starting Page	2463
Ending Page	2467
File Size	409193
Page Count	5
File Format	PDF
ISBN	9788995003893
DOI	10.1109/ICCAS.2008.4694268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-14
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	ICROS
Subject Keyword	Uncertainty Filtering Stochastic processes Nonlinear filters error covariance norm-bound uncertainty window length Upper bound Cost function Kalman filtering Computational efficiency receding horizon Kalman filters residual test Computational intelligence Testing
Content Type	Text
Resource Type	Article

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