DocumentCode :
1977721
Title :
Recursive total least squares algorithms for adaptive filtering
Author :
Davila, Carlos E.
Author_Institution :
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA
fYear :
1991
fDate :
14-17 Apr 1991
Firstpage :
1853
Abstract :
An algorithm for efficiently computing the eigenvector associated with the minimum eigenvalue of a correlation matrix is designed. This algorithm can be used to compute the total least squares (TLS) solution to the linear regression problem which yields unbiased equation-error infinite impulse response (IIR) adaptive filters. The algorithm utilizes a two-channel fast Kalman filter and requires only inner products involving L×1 vectors where L is one greater than the total number of filter coefficients. The TLS solution also results in unbiased finite impulse response (FIR) adaptive filters when the filter input is distributed by additive noise, a condition which is usually ignored but may often occur in practice
Keywords :
adaptive filters; digital filters; filtering and prediction theory; least squares approximations; IIR filters; adaptive filtering; adaptive filters; additive noise; algorithm; correlation matrix; eigenvector; filter coefficients; filter input; infinite impulse response; inner products; linear regression; minimum eigenvalue; recursive total least squares; two-channel fast Kalman filter; unbiased equation-error; Adaptive filters; Algorithm design and analysis; Eigenvalues and eigenfunctions; Equations; Filtering algorithms; Finite impulse response filter; IIR filters; Least squares methods; Linear regression; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
ISSN :
1520-6149
Print_ISBN :
0-7803-0003-3
Type :
conf
DOI :
10.1109/ICASSP.1991.150722
Filename :
150722
Link To Document :
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