SIAM Review
Convex Optimization
IIR digital filter design with new stability constraint based on argument principle
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A WISE method for designing IIR filters
IEEE Transactions on Signal Processing
A weighted least-squares method for the design of stable 1-D and2-D IIR digital filters
IEEE Transactions on Signal Processing
Least-squares approximation of FIR by IIR digital filters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IIR Approximation of FIR Filters Via Discrete-Time Vector Fitting
IEEE Transactions on Signal Processing
Multistage IIR filter design using convex stability domains defined by positive realness
IEEE Transactions on Signal Processing
The use of model reduction techniques for designing IIR filterswith linear phase in the passband
IEEE Transactions on Signal Processing
Weighted least-squares approximation of FIR by IIR digital filters
IEEE Transactions on Signal Processing
Minimax design of IIR digital filters using iterative SOCP
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Design of IIR filters using a pole-zero mapping approach
Digital Signal Processing
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This paper presents a new algorithm using semidefinite programming (SDP) relaxation to design infinite impulse response digital filters in the minimax sense. Unlike traditional design algorithms that try to directly minimize the error limit, the proposed algorithm employs a bisection searching procedure to locate the minimum error limit of the approximation error. Given a fixed error limit at each iteration, the SDP relaxation technique is adopted to formulate the design problem in a convex form. In practice, the true minimax design cannot be always obtained. Thus, a regularized feasibility problem is adopted in the bisection searching procedure. The stability of the designed filters can also be guaranteed by adjusting the regularization coefficient. Unlike other sequential design methods, the proposed algorithm tries to find a feasible solution at each iteration of the sequential design procedure within a feasible set defined by the relaxed constraints. This feasible set is not restricted within the neighborhood of a given point obtained from the previous iteration. Thus, the proposed method can avoid being trapped in the locally minimum point. Four examples are presented in this paper to demonstrate the effectiveness of the proposed method.