Almost minimax design of FIR filter using an IRLS algorithm without matrix inversion

Ruijie Zhao, Zhiping Lin*, Kar Ann Toh, Lei Sun, Xiaoping Lai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An iterative reweighted least squares (IRLS) algorithm is presented in this paper for the minimax design of FIR filters. In the algorithm, the resulted subproblems generated by the weighted least squares (WLS) are solved by using the conjugate gradient (CG) method instead of the time-consuming matrix inversion method. An almost minimax solution for filter design is consequently obtained. This solution is found to be very efficient compared with most existing algorithms. Moreover, the filtering solution is flexible enough for extension towards a broad range of filter designs, including constrained filters. Two design examples are given and the comparison with other existing algorithms shows the excellent performance of the proposed algorithm.

Original languageEnglish
Title of host publicationSecond International Workshop on Pattern Recognition
EditorsGuojian Chen, Xudong Jiang, Masayuki Arai
PublisherSPIE
ISBN (Electronic)9781510613508
DOIs
Publication statusPublished - 2017
Event2nd International Workshop on Pattern Recognition, IWPR 2017 - Singapore, Singapore
Duration: 1 May 20173 May 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10443
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2nd International Workshop on Pattern Recognition, IWPR 2017
Country/TerritorySingapore
CitySingapore
Period1/05/173/05/17

Keywords

  • Conjugate gradient method
  • FIR filters
  • IRLS algorithm
  • Minimax design

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