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

• Main Authors: Zhao, Ruijie, Lin, Zhiping, Toh, Kar-Ann, Sun, Lei, Lai, Xiaoping
• Other Authors: Jiang, Xudong
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: FIR Filter; DRNTU::Science::Biological sciences; Conjugate Gradient Method
• Online Access: https://hdl.handle.net/10356/102533; http://hdl.handle.net/10220/47263
• Institution: Nanyang Technological University

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.