Designs of Chebyshev-type complex FIR filters and digital beamformers with linear-phase characteristics

Zhang, X.; Dai, S.

doi:10.1049/ip-vis:19949707

Cited by 7 publications

(2 citation statements)

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“…Based on the above analysis, all the FIR filters design problems presented in refs. [5][6][7][8][9][10][11][12] can be solved by our SOCP approach, which shows good flexibility of SOCP.…”

Section: Design Of Fir Filtersmentioning

confidence: 99%

“…The L 1 , L 2 (or least-squares), and L∞ (or Chebyshev, Minimax) approximation criteria are commonly used. Minimax design [5,6] was used, where the peak errors were more important than the total squared errors, and least-squares design [7,8] was used, where the total squared errors were more important. When single norm approximation criterion is not appropriate in many applications, the mix-norm criterion is used, in which some constraints are imposed on the passbands or/and stopbands, e.g., the peak constrained weighted least square error (PCWLSE) FIR filters design [9,10] , equiripple passbands and peak-constrained least-squares stopbands design [11] , the mean-square stopbands (passbands) constrained least square passbands (stopbands) design [12] , and so on.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design and verification of temporal and spatial filters using second-order cone programming approach

Yan

2006

SCI CHINA SER F

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Temporal filters and spatial filters are widely used in many areas of signal processing. A number of optimal design criteria to these problems are available in the literature. Various computational techniques are also presented to optimize these criteria chosen. There are many drawbacks in these methods. In this paper, we introduce a unified framework for optimal design of temporal and spatial filters. Most of the optimal design problems of FIR filters and beamformers are included in the framework. It is shown that all the design problems can be reformulated as convex optimization form as the second-order cone programming (SOCP) and solved efficiently via the well-established interior point methods. The main advantage of our SOCP approach as compared with earlier approaches is that it can include most of the existing methods as its special cases, which leads to more flexible designs. Furthermore, the SOCP approach can optimize multiple required performance measures, which is the drawback of earlier approaches. The SOCP approach is also developed to optimally design temporal and spatial two-dimensional filter and spatial matrix filter. Numerical results demonstrate the effectiveness of the proposed approach.

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“…Based on the above analysis, all the FIR filters design problems presented in refs. [5][6][7][8][9][10][11][12] can be solved by our SOCP approach, which shows good flexibility of SOCP.…”

Section: Design Of Fir Filtersmentioning

confidence: 99%