Digital filtering is an integral part of many digital signal processing algorithms. Digital filters are characterized as either recursive [infinite impulse response (IIR)] or non-recursive [finite impulse response (FIR)] filters. IIR filters require a smaller order for the same set of specifications compared to FIR filters, while FIR filters provide a linear phase property. However, IIR filters...

Digital filters with linear phase responses, that is, constant group delay responses are needed in many applications for signal and image processing. In this paper, a novel method is proposed for designing maximally flat IIR filters with flat group delay responses in the passband. First, a system of linear equations are derived from the flatness conditions of IIR filters given in the passband a...

The linear-phase IIR filters are described in many cases, mainly due to distortion-free transmission of signals. One of the major problems of IIR filter design is stability, which can be obtained with suitable value of group delay τ . This paper concerns calculation of filter order N and group delay τ in case of quasi-equiripple design of IIR filters. We propose a novel procedure for determinin...

This paper introduces a class of two-channel hybrid analog and IIR digital filter banks that approximates perfect magnitude reconstruction. It is an extension of a recently introduced class but that uses IIR instead of FIR synthesis filters. Using IIR filters the computational complexity can be reduced. This is demonstrated by means of an example. The filters are designed by making use of linea...

Filters are used to implement signal transforms, and filter design is a very important part of the signal processing field. There are currently 2 types of filters available: the finite impulse response (FIR) filter, and the infinite impulse response (IIR) filter. While FIR filters have desirable traits such as stability (an absence of poles), IIR filters are able to generate outputs more effici...

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be b...

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability a...

We propose a new way of performing stable and more efficient F − X wavefield extrapolation for the application of seismic imaging and datuming via Infinite Impulse Response (IIR) F − X filters. To prove the concept, we used model reduction techniques to design the complex-valued IIR F −X extrapolation filters. Simulation results indicate that we can obtain stable designs using the proposed filt...

This paper determines coefficients of infinite-impulse response (IIR) using Simulated Annealing (SA). IIR filter is essentially a digital filter with Recursive responses. Since the error surface of digital IIR filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper heuristic way for the designing IIR filters is pre...