Iterative Reweighted Least-Squares Design of FIR Filters

C. Sidney Burrus; J. A. Barreto; Ivan W. Selesnick

doi:10.1109/78.330353

Iterative Reweighted Least-Squares Design of FIR Filters

C. Sidney Burrus, J. A. Barreto, Ivan W. Selesnick

Research output: Contribution to journal › Article › peer-review

Abstract

This paper develops a new iterative reweighted least squares algorithm for the design of optimal L, approximation FIR filters. The algorithm combines a variable p technique with a Newton’s method to give excellent robust initial convergence and quadratic final convergence. Details of the convergence properties when applied to the Lp optimization problem are given. The primary purpose of Lp approximation for filter design is to allow design with different error criteria in pass and stopband and to design constrained L2 approximation filters. The new method can also be applied to the complex Chebyshev approximation problem and to the design of 2-D FIR filters.

Original language	English (US)
Pages (from-to)	2926-2936
Number of pages	11
Journal	IEEE Transactions on Signal Processing
Volume	42
Issue number	11
DOIs	https://doi.org/10.1109/78.330353
State	Published - Nov 1994

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1109/78.330353

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title = "Iterative Reweighted Least-Squares Design of FIR Filters",

abstract = "This paper develops a new iterative reweighted least squares algorithm for the design of optimal L, approximation FIR filters. The algorithm combines a variable p technique with a Newton{\textquoteright}s method to give excellent robust initial convergence and quadratic final convergence. Details of the convergence properties when applied to the Lp optimization problem are given. The primary purpose of Lp approximation for filter design is to allow design with different error criteria in pass and stopband and to design constrained L2 approximation filters. The new method can also be applied to the complex Chebyshev approximation problem and to the design of 2-D FIR filters.",

author = "Burrus, {C. Sidney} and Barreto, {J. A.} and Selesnick, {Ivan W.}",

note = "Funding Information: Manuscript received December 4. 1992: revised February 24. 1994. This work was supported by Bell Northern Research. the NSF, DARPA. Funday-acuchokaspau, and by NDSEG fellowship NOO14-895-3204, The associate editor coordinating the review of this paper and approving it for publication was Dr. Robert A. Gobel. The authors are with the Electrical and Computer Engineering Department and Computational Mathematics Laboratory. Rice University. Houston. TX 7725 1-1 892 USA. IEEE Log Number 9404779.",

year = "1994",

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doi = "10.1109/78.330353",

language = "English (US)",

volume = "42",

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N2 - This paper develops a new iterative reweighted least squares algorithm for the design of optimal L, approximation FIR filters. The algorithm combines a variable p technique with a Newton’s method to give excellent robust initial convergence and quadratic final convergence. Details of the convergence properties when applied to the Lp optimization problem are given. The primary purpose of Lp approximation for filter design is to allow design with different error criteria in pass and stopband and to design constrained L2 approximation filters. The new method can also be applied to the complex Chebyshev approximation problem and to the design of 2-D FIR filters.

AB - This paper develops a new iterative reweighted least squares algorithm for the design of optimal L, approximation FIR filters. The algorithm combines a variable p technique with a Newton’s method to give excellent robust initial convergence and quadratic final convergence. Details of the convergence properties when applied to the Lp optimization problem are given. The primary purpose of Lp approximation for filter design is to allow design with different error criteria in pass and stopband and to design constrained L2 approximation filters. The new method can also be applied to the complex Chebyshev approximation problem and to the design of 2-D FIR filters.

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Iterative Reweighted Least-Squares Design of FIR Filters

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