Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/217
Title: A reorganized innovation approach to linear estimation
Authors: Zhang, H
Xie, L
Zhang, DD 
Soh, YC
Keywords: Delayed measurement
Discrete-time systems
Innovation analysis
Optimal filtering
Riccati equations
Issue Date: Oct-2004
Publisher: IEEE
Source: IEEE transactions on automatic control, Oct. 2004, v. 49, no. 10, p.1810-1814 How to cite?
Journal: IEEE transactions on automatic control 
Abstract: This note will address a linear minimum variance estimation of discrete-time systems with instantaneous and delayed measurements. Although the problem may be approached via system augmentation and standard Kalman filtering, the computation of filter may be expensive when the dimension of the system is high and the measurement lag is significant. In this note, a new tool, termed as reorganized innovation sequence, is presented for deriving the optimal filter. The optimal filter is given by two Riccati difference equations (RDEs) with the same dimension as that of the original system. The approach is shown to induce saving of computational cost as compared to the system augmentation approach, especially when the delay is large. Further, it can be extended to solving the more complicated H[sub∞] fixed-lag smoothing in Krein space.
URI: http://hdl.handle.net/10397/217
ISSN: 0018-9286
DOI: 10.1109/TAC.2004.835599
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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