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.
|Title of host publication||Second International Workshop on Pattern Recognition|
|Editors||Guojian Chen, Xudong Jiang, Masayuki Arai|
|Publication status||Published - 2017|
|Event||2nd International Workshop on Pattern Recognition, IWPR 2017 - Singapore, Singapore|
Duration: 2017 May 1 → 2017 May 3
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Other||2nd International Workshop on Pattern Recognition, IWPR 2017|
|Period||17/5/1 → 17/5/3|
Bibliographical noteFunding Information:
This research was supported partially by the National Nature Science Foundation of China under Grants 61304142, 61573123 and 61673059, and partially by the Singapore Academic Research Fund (AcRF) Tier 1 under Project RG 31/16.
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering