Normalized lattice algorithms for least-squares FIR system identification

Recently developed algorithms for least-squares identification of autoregressive models are extended in this paper so as to facilitate least-squares identification of finite impulse-response models. The algorithms belong to the class of square-root normalized lattice algorithms, hence they share the...

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Veröffentlicht in:IEEE transactions on acoustics, speech, and signal processing Jg. 31; H. 1; S. 122 - 128
Hauptverfasser: Porat, B., Kailath, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.01.1983
Schlagworte:
Computational efficiency
Contracts
Equations
Finite impulse response filter
Information systems
Laboratories
Lattices
Prediction algorithms
Smoothing methods
System identification
ISSN:0096-3518
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Zusammenfassung:Recently developed algorithms for least-squares identification of autoregressive models are extended in this paper so as to facilitate least-squares identification of finite impulse-response models. The algorithms belong to the class of square-root normalized lattice algorithms, hence they share the computational efficiency and good numerical behavior of the latter. Two versions are presented-one for identifying time-invariant models and the other for tracking time-varying parameters. New lattice-form realizations of the identified FIR models are given. The general framework is then specialized to the important cases of prediction and smoothing.
ISSN:0096-3518
DOI:10.1109/TASSP.1983.1164012