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In this paper we consider IIR filter design where both magnitude and phase are optimized using a weighted and sampled least-squares criterion. We propose a new convex stability domain defined by positive realness for ensuring the stability of the filter and adapt the Steiglitz-McBride (SM), Gauss-Newton (GN) and classical descent methods to the new(More)
We discuss a method for multidimensional FIR filter design via sum-of-squares formulations of spectral mask constraints. The sum-of-squares optimization problem is expressed as a semidefinite program with low-rank structure, by sampling the constraints using discrete cosine and sine transforms. The resulting semidefinite program is then solved by a(More)
The problem under study here is the minimax design of linear-phase lowpass FIR filters having variable passband width and implemented through a Farrow structure. We have two main contributions. The first is the design of adjustable FIR filters without discretization, using 2D positive trigonometric poly-nomials, an approach leading to semidefinite(More)
Designing sparse 1D and 2D filters has been the object of research in recent years due mainly to the developments in the field of sparse representations. The main goal is to reduce the implementation complexity of a filter while keeping as much of the performance as possible. This paper describes a new method for designing sparse filters in the minimax(More)