Design of Infinite Impulse Response (IIR) filters with almost linear phase characteristics

2003 ◽  
Author(s):  
G.D. Halikias ◽  
I.M. Jaimoukha
Keyword(s):  
Impulse Response ◽  
Infinite Impulse Response ◽  
Linear Phase ◽  
Iir Filters

scholarly journals Design of One-Dimensional Linear Phase Digital IIR Filters Using Orthogonal Polynomials

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Orthogonal Polynomials ◽  
Impulse Response ◽  
Phase 1 ◽  
Infinite Impulse Response ◽  
Linear Phase ◽  
One Dimensional ◽  
Iir Filters ◽  
Iir Filter ◽  
Amplitude Characteristics ◽  
Zeros And Poles

In the present paper, we discuss a method to design a linear phase 1-dimensional Infinite Impulse Response (IIR) filter using orthogonal polynomials. The filter is designed using a set of object functions. These object functions are realized using a set of orthogonal polynomials. The method includes placement of zeros and poles in such a way that the amplitude characteristics are not changed while we change the phase characteristics of the resulting IIR filter.

Design and Applications of Digital Filters

2011 ◽  
pp. 1016-1023
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
High Reliability ◽  
Rapid Development ◽  
Digital Signal ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Circuit Fabrication ◽  
Analog Systems

Digital signal processing (DSP) is an area of engineering that “has seen explosive growth during the past three decades” (Mitra, 2005). Its rapid development is a result of significant advances in digital computer technology and integrated circuit fabrication (Jovanovic Dolecek, 2002; Smith, 2002). Diniz, da Silva, and Netto (2002) state that “the main advantages of digital systems relative to analog systems are high reliability, suitability for modifying the system’s characteristics, and low cost”. The main DSP operation is digital signal filtering, that is, the change of the characteristics of an input digital signal into an output digital signal with more desirable properties. The systems that perform this task are called digital filters. The applications of digital filters include the removal of the noise or interference, passing of certain frequency components and rejection of others, shaping of the signal spectrum, and so forth (Ifeachor & Jervis, 2001; Lyons, 2004; White, 2000). Digital filters are divided into finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR digital filters are often preferred over IIR filters because of their attractive properties, such as linear phase, stability, and the absence of the limit cycle (Diniz, da Silva & Netto, 2002; Mitra, 2005). The main disadvantage of FIR filters is that they involve a higher degree of computational complexity compared to IIR filters with equivalent magnitude response (Mitra, 2005; Stein, 2000).

scholarly journals Design of IIR Digital Filters with Arbitrary Flatness Using Iterative Quadratic Programming

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yasunori Sugita
Keyword(s):  
Transfer Function ◽  
Digital Filters ◽  
Design Method ◽  
Infinite Impulse Response ◽  
Linear Matrix ◽  
Linear Phase ◽  
Iir Filters ◽  
Matrix Equality ◽  
Iir Digital Filters ◽  
The Stability

This paper presents a design method of Chebyshev-type and inverse-Chebyshev-type infinite impulse response (IIR) filters with an approximately linear phase response. In the design of Chebyshev-type filters, the flatness condition in the stopband is preincorporated into a transfer function, and an equiripple characteristic in the passband is achieved by iteratively solving the QP problem using the transfer function. In the design of inverse-Chebyshev-type filters, the flatness condition in the passband is added to the constraint of the QP problem as the linear matrix equality, and an equiripple characteristic in the stopband is realized by iteratively solving the QP problem. To guarantee the stability of the obtained filters, we apply the extended positive realness to the QP problem. As a result, the proposed method can design the filters with more high precision than the conventional methods. The effectiveness of the proposed design method is illustrated with some examples.

scholarly journals Transformations for FIR and IIR Filters’ Design

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 533
Author(s):  
V. N. Stavrou ◽  
I. G. Tsoulos ◽  
Nikos E. Mastorakis
Keyword(s):  
Impulse Response ◽  
Transfer Functions ◽  
Finite Impulse Response ◽  
Transformation Method ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Two Dimensional ◽  
Bilinear Transformation ◽  
One Dimensional ◽  
Iir Filters

In this paper, the transfer functions related to one-dimensional (1-D) and two-dimensional (2-D) filters have been theoretically and numerically investigated. The finite impulse response (FIR), as well as the infinite impulse response (IIR) are the main 2-D filters which have been investigated. More specifically, methods like the Windows method, the bilinear transformation method, the design of 2-D filters from appropriate 1-D functions and the design of 2-D filters using optimization techniques have been presented.

A New Class Of Infinite Impulse Response (IIR) Filters

2014 ◽  
Vol 2 (3) ◽  
pp. 28-31
Author(s):  
Deepak Gudivada ◽  
◽  
P.V. Muralidhar ◽  
Keyword(s):  
Impulse Response ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
New Class

An Adaptive Feedback Noise Control Approach Using Q-Parameterization

2002 ◽  
Author(s):  
Mark A. McEver ◽  
Daniel G. Cole ◽  
Robert L. Clark
Keyword(s):  
Impulse Response ◽  
Gradient Descent ◽  
Noise Control ◽  
Finite Impulse Response ◽  
Open Loop ◽  
Infinite Impulse Response ◽  
Feedback Signal ◽  
Control Approach ◽  
Iir Filters ◽  
On Line

An algorithm is presented which uses adaptive Q-parameterized compensators for control of sound. All stabilizing feedback compensators can be described in terms of plant coprime factors and a free parameter, Q, which can be any stable function. By generating a feedback signal containing only disturbance information, the parameterized compensator allows Q to be designed in an open-loop fashion. The problem of designing Q to yield desired noise reduction is formulated as an on-line gradient descent-based adaptation process. Coefficient update equations are derived for different forms of Q, including digital finite impulse response (FIR) and lattice infinite impulse response (IIR) filters. Simulations predict good performance for both tonal and broadband disturbances, and a duct feedback noise control experiment results in a 37 dB tonal reduction.

Digital DC blocker filters

Frequenz ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Klaus Tittelbach-Helmrich
Keyword(s):  
Impulse Response ◽  
Transfer Functions ◽  
Cutoff Frequency ◽  
Special Kind ◽  
Infinite Impulse Response ◽  
Digital Signals ◽  
Low Frequencies ◽  
Iir Filters

Abstract This paper mathematically investigates a special kind of digital infinite-impulse response (IIR) filters, suitable for filtering out very low frequencies near zero from digital signals. We investigate the transfer functions of such filters from 1st to 3rd order and provide formulas to calculate the filter coefficients from the desired cutoff frequency.

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