NDLI: QR methods of O(N) complexity in adaptive parameter estimation

Content Provider	IEEE Xplore Digital Library
Author	Zheng-She Liu
Copyright Year	1991
Abstract	Recent attention in adaptive least squares parameter estimation has been focused on methods derived from the QR factorization owing to the fact that the QR-based algorithms are much more numerically stable and accurate than the traditional pseudo-inverse-based algorithms, also known as normal equation-based algorithms, even though the former is usually much slower than the latter. This paper presents a fast adaptive least squares algorithm for the parameter estimation of linear and some nonlinear time-varying systems. The algorithm is based on Householder transformations. As verified by simulation results, this algorithm exhibits good numerical stability and accuracy. In addition, the new algorithm requires computation and storage with order of O(N) rather than O(N/sup 2/) where N is the number of unknown parameters to be estimated. This algorithm can be easily extended to construct other kinds of algorithms, such as the generalized adaptive least squares algorithm, the augmented matrix algorithm, and the maximum likelihood algorithm.<>
Sponsorship	IEEE Signal Processing Society
Starting Page	720
Ending Page	729
Page Count	10
File Size	695303
File Format	PDF
ISSN	1053587X
Volume Number	43
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Finite impulse response filter Least squares approximation Adaptive algorithm Filtering algorithms Numerical stability Least squares methods Computational complexity Nonlinear equations Time varying systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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