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Proceedings Paper

Almost minimax design of FIR filter using an IRLS algorithm without matrix inversion
Author(s): Ruijie Zhao; Zhiping Lin; Kar-Ann Toh; Lei Sun; Xiaoping Lai
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Paper 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.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431F (19 June 2017); doi: 10.1117/12.2280405
Show Author Affiliations
Ruijie Zhao, Shandong Univ. (China)
Nanyang Technological Univ. (Singapore)
Zhiping Lin, Nanyang Technological Univ. (Singapore)
Kar-Ann Toh, Yonsei Univ. (Korea, Republic of)
Lei Sun, Beijing Institute of Technology (China)
Xiaoping Lai, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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