The normalized least-squares order-recursive lattice smoother

This paper introduces a variance- and angle-normalized version of the least-squares order-recursive lattice (LSORL) smoother, say the normalized LSORL smoother, via a geometric approach. The normalized LSORL smoother has excellent round-off noise properties and inherits all the other advantages of t...

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Veröffentlicht in:Signal processing Jg. 82; H. 6; S. 895 - 905
Hauptverfasser: Kim, Dong Kyoo, Park, PooGyeon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.06.2002
Elsevier Science
Schlagworte:
Adaptive filters
Applied sciences
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Finite precision arithmetic
Geometric interpretation
Information, signal and communications theory
Least-squares lattice algorithms
Normalized LSORL smoothers
Order-recursive structures
Signal and communications theory
Signal, noise
Smoothing algorithms
Telecommunications and information theory
Least-squares lattice algorithms
Smoothing algorithms
Order-recursive structures
Adaptive filters
Geometric interpretation
Normalized LSORL smoothers
Finite precision arithmetic
Simulation
Least squares method
Adaptive filtering
Recursive method
Signal processing
Geometrical method
Algorithm
Lattice structure
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper introduces a variance- and angle-normalized version of the least-squares order-recursive lattice (LSORL) smoother, say the normalized LSORL smoother, via a geometric approach. The normalized LSORL smoother has excellent round-off noise properties and inherits all the other advantages of the normalized LSORL as well. Simulation results show that the normalized LSORL smoother outperforms the existing LSORL and QRD-based least-squares lattice smoother algorithms when finite-precision arithmetic is used.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0165-1684
1872-7557
DOI:10.1016/S0165-1684(02)00199-8