Constrained least squares design of 2-D FIR filters

Lang, Markus; Selesnick, Ivan; Burrus, C.S.

doi:10.1109/78.502335

Cited by 31 publications

(20 citation statements)

References 20 publications

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“…A 2-D PCLS filter design algorithm was proposed in [14]. However, the algorithm proposed in [14] is not guaranteed to converge, and it is only a single exchange algorithm.…”

Section: Generalized Multiple Exchangesmentioning

confidence: 99%

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Peak-constrained least-squares optimization

Adams

Sullivan

1998

IEEE Trans. Signal Process.

104

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“…A 2-D PCLS filter design algorithm was proposed in [14]. However, the algorithm proposed in [14] is not guaranteed to converge, and it is only a single exchange algorithm.…”

Section: Generalized Multiple Exchangesmentioning

confidence: 99%

“…However, the algorithm proposed in [14] is not guaranteed to converge, and it is only a single exchange algorithm. Single exchange algorithms are very slow in 2-D problems.…”

Section: Generalized Multiple Exchangesmentioning

confidence: 99%

Peak-constrained least-squares optimization

Adams

Sullivan

1998

IEEE Trans. Signal Process.

104

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“…subject to the constraints [9] |E| 6 (9) where is a tolerance scheme for the frequency response error. These constraints can be written in compact form as…”

Section: Problem Formulationmentioning

confidence: 99%

Design of linear phase FIR filters via linear complementarity problem approach

Nuseirat

2005

Int. J. Circ. Theor. Appl.

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SUMMARYIn this paper, the design of constrained linear phase ÿnite impulse response (FIR) ÿlters is considered. The problem is formulated as a linear complementarity problem (LCP), which is solved using Lemke's algorithm. The LCP is a reÿned mathematical formalism with useful theoretical results. The digital ÿlters presented meet e ciently the speciÿcations of the magnitude response error. The used algorithm is a direct one and therefore, there is no need for matrix inversion. However, in the iterative methods that are frequently used, the bulk of the design computation is concerned with the evaluation of matrix inversion in order to solve a system of equations. Examples to illustrate the proposed method are presented.

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“…Most existing algorithms for optimal designs of 2-D FIR filters, say, those in Algazi et al (1986), Dumitrescu (2006), Gislason et al (1993), Lai (2009), Lai et al (2009), Lang et al (1996), Lu (2002), Lu andHinamoto (2006, 2011), Rusu and Dumitrescu (2012), rearrange the coefficient matrices into vectors and then solve the design problems using algorithms for one-dimensional (1-D) FIR filters. This leads to high computational complexities of the design algorithms and much design time for large size filters.…”

Section: Introductionmentioning

confidence: 99%

A fast design algorithm for elliptic-error and phase-error constrained LS 2-D FIR filters

Hong

Lai

Zhao

2015

Multidim Syst Sign Process

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Two-dimensional (2-D) nonlinear-phase finite impulse response (FIR) filters have found many applications in signal processing and communication systems. This paper considers the elliptic-error and phase-error constrained least-squares design of 2-D nonlinear-phase FIR filters, and develops a matrix-based algorithm to solve the design problem directly for the filter's coefficient matrix rather than vectorizing it first as in the conventional methods. The matrix-based algorithm makes the design to consume much less design time than existing algorithms. Design examples and comparisons with existing methods demonstrate the effectiveness and high efficiency of the proposed design method.

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Constrained least squares design of 2-D FIR filters

Cited by 31 publications

References 20 publications

Peak-constrained least-squares optimization

Peak-constrained least-squares optimization

Design of linear phase FIR filters via linear complementarity problem approach

A fast design algorithm for elliptic-error and phase-error constrained LS 2-D FIR filters

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