WLS IIR digial filter design using SOCP

In this paper, we propose an iterative method for designing IIR digital filters in the weighted least squares (WLS) sense. Since the original design problem is essentially nonconvex, it is first relaxed into a second-order cone programming (SOCP) problem. By solving the relaxed problem, the lower bound on the optimal value of the original problem can be obtained. And the corresponding filter coefficients can be chosen as the starting point of the following iterative procedure. At each iteration, a linear inequality constraint is further incorporated to gradually reduce the gap between the original and the relaxed problem. Analyses show that the convergence of the proposed iterative procedure can be definitely guaranteed. Two examples are presented to demonstrate the effectiveness of the proposed method.

[1]  Wu-Sheng Lu Design of stable IIR digital filters with equiripple passbands and peak-constrained least-squares stopbands , 1999 .

[2]  Chien-Cheng Tseng,et al.  A weighted least-squares method for the design of stable 1-D and 2-D IIR digital filters , 1998, IEEE Trans. Signal Process..

[3]  Hon Keung Kwan,et al.  IIR Digital Filter Design with Novel Stability Criterion Based on Argument Principle , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[4]  Andrzej Tarczynski,et al.  A WISE method for designing IIR filters , 2001, IEEE Trans. Signal Process..

[5]  Wu-Sheng Lu,et al.  Design of stable minimax IIR digital filters using semidefinite programming , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[6]  C. Tseng,et al.  Minimax design of stable IIR digital filter with prescribed magnitude and phase responses , 2002 .

[7]  W.-S. Lu Design of stable IIR digital filters with equiripple passbands and peak-constrained least squares stopbands , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.

[8]  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.

[9]  Chien-Cheng Tseng Design of stable IIR digital filter based on least P-power error criterion , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[10]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[11]  L. Rabiner,et al.  Linear programming design of IIR digital filters with arbitrary magnitude function , 1974 .

[12]  Graham A. Jullien,et al.  A linear programming approach to recursive digital filter design with linear phase , 1982 .

[13]  Mathias C. Lang,et al.  Least-squares design of IIR filters with prescribed magnitude and phase responses and a pole radius constraint , 2000, IEEE Trans. Signal Process..