QRD-based sliding window adaptive LS lattice algorithms

General order-recursive least-squares estimation employing a sliding window is described. It is shown that time and order updates of any order-recursive sliding window least-squares algorithm can be obtained solely by 3*3 hyperbolic Householder transformations. Applying this general observation to t...

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Published in[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 4; pp. 49 - 52 vol.4
Main Authors Zhao, K., Ling, F., Lev-Ari, H., Proakis, J.G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1992
Subjects
Covariance matrix
Digital signal processing
Dynamic range
Equations
Lattices
Matrix decomposition
Signal generators
Signal processing
Signal processing algorithms
Vectors
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ISBN9780780305328
0780305329
ISSN1520-6149
DOI10.1109/ICASSP.1992.226414

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Summary:General order-recursive least-squares estimation employing a sliding window is described. It is shown that time and order updates of any order-recursive sliding window least-squares algorithm can be obtained solely by 3*3 hyperbolic Householder transformations. Applying this general observation to the sliding window least-squares estimation of time-series signals results in a new algorithm: the hyperbolic Householder lattice (HHL) algorithm. This work broadens the range of applications of QR-decomposition (QRD)-based adaptive least-squares algorithms by allowing a sliding window formulation in addition to the known exponentially windowed form.< >
ISBN:9780780305328
0780305329
ISSN:1520-6149
DOI:10.1109/ICASSP.1992.226414