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...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 46; no. 9; pp. 2562 - 2565
Main Authors: Harteneck, M., Stewart, R.W.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.09.1998
Institute of Electrical and Electronics Engineers
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1053-587X
DOI:10.1109/78.709547