DocumentCode
2890337
Title
Regularized fast recursive least squares algorithms
Author
Houacine, Amrane
Author_Institution
Inst. of Electron., Univ. of Sci. & Technol. of Algiers, Algeria
fYear
1990
fDate
3-6 Apr 1990
Firstpage
1587
Lastpage
1590
Abstract
Chandrasekhar type factorization is used to develop new fast recursive least squares (FRLS) algorithms for finite memory filtering. Statistical priors are used to get a regularized solution which presents better numerical stability properties than that of the conventional least squares one. The algorithms presented have a unified matrix formulation, and their numerical complexity is related to the factorization rank and then depends on the a priori solution covariance matrix used. Simulation results are presented to illustrate the approach
Keywords
filtering and prediction theory; least squares approximations; matrix algebra; Chandrasekhar type factorization; covariance matrix; factorization rank; fast recursive least squares algorithms; finite memory filtering; numerical complexity; numerical stability; regularized solution; simulation results; unified matrix formulation; Covariance matrix; Equations; Filtering algorithms; Least squares approximation; Least squares methods; Numerical stability; Recursive estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location
Albuquerque, NM
ISSN
1520-6149
Type
conf
DOI
10.1109/ICASSP.1990.115725
Filename
115725
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