Sparse FIR Filter Design via Partial 1-Norm Optimization

In this brief, we consider a sparse linear-phase FIR filter design problem. Recent methods assume that all the coefficients can be nullified and, thus, various 0 or 1-norm-based optimization techniques are applied on each of them. In contrast, the proposed algorithm is based on two important observations: 1) Given design specifications, some coefficients cannot be nullified, otherwise the specifications cannot be satisfied. 2) Impulse responses on neighboring positions of an FIR filter cannot vary dramatically so as to guarantee the smoothness of the corresponding magnitude responses over most of frequencies. In view of these facts, several rules are adopted in the proposed algorithm to select indices of potential zero coefficients to be used in 1-norm optimization. Simulation results have demonstrated the effectiveness of the proposed design algorithm.

[1]  Takao Hinamoto,et al.  A Unified Approach to the Design of Interpolated and Frequency-Response-Masking FIR Filters , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[3]  Thomas A. Baran,et al.  Linear Programming Algorithms for Sparse Filter Design , 2010, IEEE Transactions on Signal Processing.

[4]  Hon Keung Kwan,et al.  Sparse FIR filter design via partial L1 optimization , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[5]  Ning Xu,et al.  Efficient WLS Design of IIR Digital Filters Using Partial Second-Order Factorization , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  R. Hartley Subexpression sharing in filters using canonic signed digit multipliers , 1996 .

[7]  Chip-Hong Chang,et al.  Contention resolution algorithm for common subexpression elimination in digital filter design , 2005, IEEE Trans. Circuits Syst. II Express Briefs.

[8]  L. Rabiner,et al.  A computer program for designing optimum FIR linear phase digital filters , 1973 .

[9]  Yong Ching Lim,et al.  Design of Linear Phase FIR Filters in Subexpression Space Using Mixed Integer Linear Programming , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[10]  Andreas Antoniou,et al.  Digital Filters: Analysis, Design and Applications , 1979 .

[11]  Shunsuke Ono,et al.  Joint Sparsity and Order Optimization Based on ADMM With Non-Uniform Group Hard Thresholding , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Ning Xu,et al.  Design of Sparse FIR Filters With Joint Optimization of Sparsity and Filter Order , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  Hon Keung Kwan,et al.  Minimax Design of IIR Digital Filters Using Iterative SOCP , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  Zhiping Lin,et al.  A Sequential Minimization Procedure for Minimax Design of IIR Filters Based on Second-Order Factor Updates , 2011, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Hon Keung Kwan,et al.  Peak-Error-Constrained Sparse FIR Filter Design Using Iterative SOCP , 2012, IEEE Transactions on Signal Processing.

[16]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[17]  K. S. Yeung,et al.  Design of FIR digital filters with prescribed flatness and peak error constraints using second-order cone programming , 2005, IEEE Transactions on Circuits and Systems II: Express Briefs.

[18]  Hon Keung Kwan,et al.  FIR, Allpass, and IIR Variable Fractional Delay Digital Filter Design , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[19]  Hon Keung Kwan,et al.  WLS Design of Sparse FIR Digital Filters , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.