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

Ruijie Zhao; Zhiping Lin; Kar-Ann Toh; Lei Sun; Xiaoping Lai

doi:10.1117/12.2280405

19 June 2017 Almost minimax design of FIR filter using an IRLS algorithm without matrix inversion

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

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 104431F (2017) https://doi.org/10.1117/12.2280405
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

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.

Citation Download Citation

Ruijie Zhao, Zhiping Lin, Kar-Ann Toh, Lei Sun, and Xiaoping Lai "Almost minimax design of FIR filter using an IRLS algorithm without matrix inversion", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431F (19 June 2017); https://doi.org/10.1117/12.2280405

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