Adaptive IIR filtering using QR matrix decomposition

In this correspondence, an approach to adaptive IIR filtering based on a pseudo-linear regression and applying an iterative QR matrix decomposition is developed. In simulations, the algorithm has shown a high stability and excellent convergence properties. The derivation of the IIR-QR adaptive filte...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on signal processing Ročník 46; číslo 9; s. 2562 - 2565
Hlavní autori: Harteneck, M., Stewart, R.W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.09.1998
Institute of Electrical and Electronics Engineers
Predmet:
Adaptive filters
Applied sciences
Computational complexity
Computational modeling
Convergence
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering
IIR filters
Information, signal and communications theory
Iterative algorithms
Iterative methods
Matrix decomposition
Signal and communications theory
Signal, noise
Stability
Telecommunications and information theory
Infinite impulse response filter
Linear regression
Matrix calculus
Signal processing
Adaptive filter
Computational complexity
Recursive filtering
ISSN:1053-587X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this correspondence, an approach to adaptive IIR filtering based on a pseudo-linear regression and applying an iterative QR matrix decomposition is developed. In simulations, the algorithm has shown a high stability and excellent convergence properties. The derivation of the IIR-QR adaptive filter is straightforward, and the computational complexity of the algorithm is comparable with the FIR-QR adaptive algorithm of the same order and less than gradient-based adaptive IIR filters such as the simplified-gradient recursive prediction error (RPE) algorithm. Fast versions of O(N) computational complexity are readily available.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
DOI:10.1109/78.709547