具有最大平坦和切比雪夫幅度响应的线性相位IIR积分器的设计

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Ivan Krstić , Goran Stančić , Jasna Radulović
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引用次数: 0

摘要

本文提出了两种设计线性相位无限脉冲响应积分器的方法。第一种方法被称为最大平坦化方法,它对频率响应误差函数施加平坦化条件,导致必须求解线性方程组以确定未知系数。此外,建立了所提出的最大平面积分器与利用基于代数多项式的正交规则导出的现有整数阶线性相位积分器之间的关系,证明后者代表了所提出的积分器的特殊情况。第二种方法,即最优方法,通过一种快速收敛的高效交换算法,使加权切比雪夫意义上的复频响误差函数最小。并将所提出的线性相位积分器与现有的几种线性和近似线性相位积分器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of linear-phase IIR integrators with maximally-flat and Chebyshev magnitude responses
This paper proposes two methods for designing linear-phase infinite impulse response integrators. The first method, referred to as the maximally-flat one, imposes flatness conditions on the frequency response error function, leading to a system of linear equations that have to be solved to determine unknown coefficients. Furthermore, a relation is established between the proposed maximally-flat integrators and existing integer-order linear-phase integrators derived using the algebraic polynomial-based quadrature rules, demonstrating that the latter represent special cases of the proposed integrators. The second method, referred to as the optimal one, minimizes the complex frequency response error function in the weighted Chebyshev sense, which is achieved by an efficient exchange algorithm that exhibits rapid convergence. The proposed linear-phase integrators are also compared with several existing linear- and nearly linear-phase integrators.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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