A Well-Conditioned Quadratic Program for Unique Design of Two Dimensional Weighted Chebyshev FIR Filters

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Sven Nordebo, Ingvar Claesson
Title: A Well-Conditioned Quadratic Program for Unique Design of Two Dimensional Weighted Chebyshev FIR Filters
Conference name: The 1996 IEEE International Conference on Acoustics, Speech and Signal Processing : conference proceedings : May 7-10, 1996, Marriott Marquis Hotel, Atlanta, Georgia, USA
Year: 1996
Pagination: 1355-1358
ISBN: 0-7803-3192-3 (hft.) 0-7803-3193-1 (inb.) 0-7803-3194-X (mikrofiche) 0-7803-3195-8 (cd-rom)
Publisher: IEEE
City: Atlanta, GA, USA
Organization: Blekinge Institute of Technology
Department: Dept. of Signal Processing (Institutionen för signalbehandling)
Dept. of Signal Processing S-372 25 Ronneby
+46 455 780 00
http://www.hk-r.se/isb/isb_en.html
Authors e-mail: Sven.Nordebo@isb.hk-r.se
Language: English
Abstract: The weighted Chebyshev design of two-dimensional FIR filters is in general not unique since the Haar condition is not generally satisfied. However, for a design on a discrete frequency domain, the Haar condition might be fulfilled. The question of uniqueness is, however, rather extensive to investigate. It is therefore desirable to define some simple additional constraints to the Chebyshev design in order to obtain a unique solution. The weighted Chebyshev solution of minimum Euclidean filter weight norm is always unique, and represents a sensible additional constraint since it implies minimum white noise amplification. It is shown that this unique Chebyshev solution can always be obtained by using an efficient quadratic programming formulation with a strictly convex objective function and linear constraints An example where a conventional Chebyshev solution is non-unique is discussed in the paper.
Subject: Signal Processing\Filter Design
Keywords: Chebyshev filters, filtering theory, FIR filters, frequency-domain synthesis, quadratic programming, two-dimensional digital filters, white noise
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