Greedy Algorithm for the Design of Linear-Phase FIR Filters with Sparse Coefficients

Fraser, Michael; Yu, Ya Jun

doi:10.1007/s00034-015-0122-5

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…As discussed in Section 1, in addition to minimum ripples in pass band and stop band, low complexity is also an important factor in properly designed filter. For this, sparsity is added to minimize the number of nonzero coefficients 36 . For a filter with symmetrical coefficient s(n), the frequency response of the sparse FIR filter is given as

F_{s} ((), ω) = \sum_{n = 0}^{N} s_{n} o_{n} e^{- italicjwn}

where

ω

represents the frequency in range

([],, 0, π)

.…”

Section: Problem Formulation Of Multiobjective Sparse Fir Filtermentioning

confidence: 99%

Low complexity multiobjective finite impulse response filter design using Salp swarm algorithm and its improved version

Srivastava

Dwivedi

Nagaria

2021

Int J Numerical Modelling

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Recent trend indicates that the assessment of low complexity is crucial for an effectual design of digital filter. Earlier the existing algorithms solely focused on reducing the pass band ripples and the stop band ripples in spite of the requirement of low implementation complexity in finite impulse response (FIR) filter design. In this regard, the optimization‐based FIR filter using multiobjective improved Salp swarm optimization algorithm have been designed. The proposed method not only minimizes the ripples of pass band and stop band but also minimizes the transition band, number of sparsity, which is used to implement a FIR filter that abides the specified frequency. The nondominated solution of filter design has a set of an optimum solution as pareto front that maintains the trade‐off between the various specifications. The designed FIR filter has been compared with other reported method to validate the applicability of the proposed method. The evaluation and analysis have been performed regrading minimum pass band ripples, minimum stop band ripple, minimum transition band, and implementation complexity. To prove the practical applicability of the proposed method, the designed FIR filters have been implemented in Xilinx ISE14.7 (Virtex‐VII) environment. The comparison result shows that the proposed method performs better than all other reported method in achieving minimum ripples and implementation complexity.

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F_{s} ((), ω) = \sum_{n = 0}^{N} s_{n} o_{n} e^{- italicjwn}

where

ω

represents the frequency in range

([],, 0, π)

.…”

Section: Problem Formulation Of Multiobjective Sparse Fir Filtermentioning

confidence: 99%

Low complexity multiobjective finite impulse response filter design using Salp swarm algorithm and its improved version

Srivastava

Dwivedi

Nagaria

2021

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…Наиболее подходящими для этой цели во многих отношениях являются КИХ-фильтры [1]. Простейшими в этом классе, с точки зрения реализации, являются инверсные КИХ-фильтры [2].…”

Section: самарский национальный исследовательский университет имени аunclassified

Internet technology for correction of the dynamic distortions in images obtained by mobile devices

Fursov¹,

Yakimov²

2017

Proceedings of 19th Scientific Conference “Scientific Services &Amp; Internet – 2017”

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“…However, several approximate methods have been developed. Many of them utilize l0-norm and estimate the positions of zero coefficients by using greedy algorithms [12], [13], integer programming [2], [14], iterative second order cone programming [15], iterative shrinkage/thresholding [16], and genetic algorithms [17]. If l0-norm in (1) is replaced by l1-norm, the filter is obtained with many small coefficients [5].…”

Section: Introductionmentioning

confidence: 99%

Sparse FIR Filter Design Based on Signomial Programming

Bellotti

Vučić

2020

ELEKTRON ELEKTROTECH

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The goal of sparse FIR filter design is to minimize the number of nonzero filter coefficients, while keeping its frequency response within specified boundaries. Such a design can be formally expressed via minimization of l0-norm of filter’s impulse response. Unfortunately, the corresponding minimization problem has combinatorial complexity. Therefore, many design methods are developed, which solve the problem approximately, or which solve the approximate problem exactly. In this paper, we propose an approach, which is based on the approximation of the l0-norm by an lp-norm with 0 < p < 1. We minimize the lp-norm using recently developed method for signomial programming (SGP). Our design starts with forming a SGP problem that describes filter specifications. The optimum solution of the problem is then found by using iterative procedure, which solves a geometric program in each iteration. The filters whose magnitude responses are constrained in minimax sense are considered. The design examples are provided illustrating that the proposed method, in most cases, results in filters with higher sparsity than those of the filters obtained by recently published methods.

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Greedy Algorithm for the Design of Linear-Phase FIR Filters with Sparse Coefficients

Cited by 13 publications

References 19 publications

Low complexity multiobjective finite impulse response filter design using Salp swarm algorithm and its improved version

Low complexity multiobjective finite impulse response filter design using Salp swarm algorithm and its improved version

Internet technology for correction of the dynamic distortions in images obtained by mobile devices

Sparse FIR Filter Design Based on Signomial Programming

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