FIR Filter Design by Convex Optimization Using Directed Iterative Rank Refinement Algorithm

Abstract

The advances in convex optimization techniques have offered new formulations of design with improved control over the performance of FIR filters. By using lifting techniques, the design of a length-FIR filter can be formulated as a convex semidefinite program (SDP) in terms of an matrix that must be rank-1. Although this formulation provides means for introducing highly flexible design constraints on the magnitude and phase responses of the filter, convex solvers implementing interior point methods almost never provide a rank-1 solution matrix. To obtain a rank-1 solution, we propose a novel Directed Iterative Rank Refinement (DIRR) algorithm, where at each iteration a matrix is obtained by solving a convex optimization problem. The semidefinite cost function of that convex optimization problem favors a solution matrix whose dominant singular vector is on a direction determined in the previous iterations. Analytically it is shown that the DIRR iterations provide monotonic improvement, and the global optimum is a fixed point of the iterations. Over a set of design examples it is illustrated that the DIRR requires only a few iterations to converge to an approximately rank-1 solution matrix. The effectiveness of the proposed method and its flexibility are also demonstrated for the cases where in addition to the magnitude constraints, the constraints on the phase and group delay of filter are placed on the designed filter. Index Terms-Finite impulse response (FIR) filter design, spectral mask, convex optimization, semidefinite programming, semidefinite relaxation, iterative rank refinement. I. INTRODUCTION D IGITAL FINITE IMPULSE RESPONSE (FIR) filters have always been one of the prominent building blocks in digital signal processing because of their assured stability and efficient implementations based on the Fast Fourier Transform (FFT) [1]-[3]. A diverse class of FIR filter design techniques have been proposed in the literature including the Parks-McClellan algorithm [2], [4], optimization based techniques like METEOR [5] and peak-constrained least squares (PCLS)

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