Designing a Low- Pass Fir Digital Filter by Using Hamming Window and Blackman Window Technique

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 efficiently. This paper combines the two types of filters by introducing an output slope trigger, to make a hybrid impulse response (HIR) filter with desirable traits from both FIR and IIR filters

Recursive structures are introduced for implementing long linear-phase finite impulse response (FIR) filters using a very small number of multipliers. The implementation uses the principle of switching and resetting between two identical copies of the same infinite impulse response (IIR) filter. The impulse response of the resulting filters is a truncated and shifted version of the response of a filter G(z)G(z/sup -1/) where G(z) is a stable IIR stable and G(z/sup -1/) is the corresponding unstable IIR filter. The filters are implemented as a parallel connection of several branches, each branch generating a truncated response corresponding to a complex conjugate pole pair and its mirror-image pair. The truncation is performed using a feedforward term which provides pole-zero cancellations. To stabilize the pole-zero cancellations, and to avoid the quantization error from growing too much, the branch filters are implemented by applying the principle of switching and resetting. It is shown, by means of an example, that by using this approach a nearly optimum FIR filter of order larger than 500 using just 17 adjustable parameters can be designed. >

One of the main purposes of digital filtering is to improve the quality of the signal. In this chapter, we give an overview of digital filtering. This often uses transformations in order to maintain the desired information in the presence of undesired signals that could corrupt it. Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters are linear time invariant filters because the coefficients of the filters do not vary with time (i.e., they are time invariant) as well as because the signal is decomposed into a linear combination of basic signals (i.e., the linearity property). The FIR filter is non-recursive as this does not have feedback. In contrast the IIR filter has a recursive component because of feedback. The application of FIR filters to signal processing is more computationally involved than with IIR filters because FIR needs more coefficients than IIR filters.

Repeaters equipped with on-board digital baseband processing in a time division duplex (TDD) demand short processing time in order to alleviate inter-symbol interference resulting from having a time delay that is greater than the guard time. To accomplish this, the total system delay of the repeater should be minimized as much as possible without distorting signal quality. Conventionally, the finite impulse response (FIR) type of filter is deployed as a channelization filter, but due to the necessity of large numbers of coefficients to fulfill a prerequisite filter response with a sharp transition band characteristic, an unwanted excessive time delay intrinsically occurs. To make the processing delay as low as possible, this paper proposes a method employing a minimum-phase characterized infinite impulse response (IIR) filter whose magnitude response is almost identical to that of the original FIR filter. Furthermore, in order to linearize the phase response of the designed IIR filter, this paper also introduces an all-pass filter cascaded with the IIR filter for digital down-conversion as well as up-conversion. To achieve further simplicity, this paper introduces polyphase-style IIR filters transformed from conventional single IIR filters that have their own all-pass filters in order to linearize the phase response. The computer simulation results verify that the proposed integrated IIR filter exhibits a relatively short processing delay with a minor deterioration in signal quality-like error vector magnitude (EVM) performance.

An algorithm for multichannel adaptive IIR (infinite impulse response) filtering is presented and applied to the active control of broadband random noise in a small reverberant room. Assuming complete knowledge of the primary noise, the theoretically optimal reductions of acoustic energy are initially calculated by means of a frequency-domain model. These results are contrasted with results of a causality constrained theoretical time-domain optimization which are then compared with experimental results, the latter two results showing good agreement. The experimental performances of adaptive multichannel FIR (finite impulse response) and IIR filters are then compared for a four-secondary-source, eight-error microphone active control system, and it is found that for the present application FIR filters are sufficient when the primary noise source is a loudspeaker. Some experiments are then presented with the primary noise field generated by a panel excited by a loudspeaker in an adjoining room. These results show that far better performances are provided by IIR and FIR filters when the primary source has a lightly damped dynamic behavior which the active controller must model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Time-interleaved analog-to-digital converter (TIADC) mismatch compensation filters are traditionally finite impulse response (FIR) filters. The advantages of using FIR filters are that FIR filters are inherently stable and that the design techniques are readily available. In this paper, we show that, infinite impulse response (IIR) mismatch compensation filters with guaranteed stability can be designed and for the same total number of coefficients, the IIR filters achieve better mismatch compensation than their FIR counterparts. We also show that for a given spurious frequency power, our new IIR TIADC compensation filter yields more than 30% saving in the total number of multiplier coefficients when compared to the conventional FIR TIADC compensation filters.

This paper describes a new approach to the design of multidimensional (M-D) finite-wordlength digital filters with specifications in the frequency and spatial domains. The approach is based on stochastic optimization and extends previous work on finite impulse response (FIR) filters in two ways: by inclusion of spatial constraints and by application to the case of infinite impulse response (IIR) filters. The formulation proposed is based on a multiple-term objective function that, in addition to magnitude constraints, also includes step response, group delay and stability constraints. Our attention to these characteristics stems from the application of such filters to video processing that we are actively pursuing. Since filter coefficients are of finite precision and since the objective function is multivariable, nondifferentiable and likely to have multiple minima, we use simulated annealing for optimization. We show numerous examples of the design of practical filters such as channel and luminance/chrominance separation filters used in the NTSC system. We demonstrate the impact of coefficient precision as well as of group delay and step response constraints on filter parameters. >

For multi-track read channel of high areal density magnetic recording systems, two-dimensional (2-D) equalizer is normally considered to shape the readback signal to the desired 2-D generalized partial response (GPR) target. However, the equalizer can correlate the additive noise in the signal and the noise can become the correlated/colored noise which will have a negative impact on the performance of the detector. In the literature, a noise predictive detector using a linear noise predictor (a finite impulse response (FIR) filter) is proposed to tackle the performance loss due to the color/correlated noise. Integrating noise prediction into a detector can grow the complexity of the detector depending on the number of taps in the FIR filter. Alternatively, the noise predictor can also be implemented using an infinite impulse response (IIR) filter. Moreover, IIR predictor can provide the performance gain without requiring a large number of filter's taps. Therefore, in this paper, we study the performance of a noise predictive multi-track joint detector using an IIR filter for the multi-track read channel of high areal density bit-patterned media recording (BPMR) systems. We evaluate its performance against with the multi-track joint detectors with and without conventional noise predictors using FIR filter.

Finite impulse response digital filters

A filter can need to have a certain frequency response, or a particular reaction to an impulse, step, or ramp, or mimic an analogue system. Digital filters can be categorised into Finite Impulse Response (FIR) filters &amp; Infinite Impulse Response (IIR) filters depending on the system response. The thesis addresses FPGA low pass FIR filter design. Theoretical and experimental findings conducted FIR low pass filter point to the window design approach as very straightforward and user-friendly due to the presence of well-defined equation. Comparison indicated that the Direct-Form structure technique is simpler and performs better than other typical filter structures whereas Kaiser window gives the minimal main-lobe width and a sharp cut-off indicating smaller transition width. Experimental research of coefficient quantisation reveals a link between the frequency response, number of coefficients, and bit count. Key words: Digital Signal Processing (DSP),Finite Impulse Response (FIR) Filter, Low- Pass Filter, Verilog HDL, Windowing Techniques, Hamming Window, Hanning Window.

This paper presents a method for the weighted least-squares approximation of finite impulse response (FIR) filters by infinite impulse response (IIR) filters. It is shown, how a solution to this approximation problem can be obtained by solving a related pure least-squares approximation problem. For the latter, we utilize a generalized version of a previously published technique with low computational complexity and guaranteed stability of the IIR filters. Unlike the well-established model-reduction approaches that are carried out in the state space, our method works directly with the numerator and denominator coefficients of the transfer functions. Thus, the influence of finite-precision arithmetic on the results is small. This makes our approach applicable for the approximation of large-order FIR filters and allows the usage of arbitrarily shaped weighting functions. It is shown that our method can successfully be employed to achieve a uniform approximation.

Digital infinite impulse response (IIR) filter have become the target of growing interest, because they often provide a much better performance and less computational cost than finite impulse response (FIR) filters. Since the problem of error surface of designing Digital IIR filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper, an evolutionary method based on Imperialist Competitive Algorithm (ICA) has been proposed to design Digital IIR filters. By adding a step to the standard ICA algorithm, its performance has been improved in searching solution space and convergence to the global minima. Simulation results show the efficiency of the proposed method to design Digital IIR filter.

Fractional sample interpolation with finite impulse response (FIR) filters is commonly used for motion-compensated prediction (MCP). The FIR filtering can be viewed as a signal decomposition using restricted basis functions. The concept of generalized interpolation provides a greater degree of freedom for selecting basis functions. We developed a generalized interpolation framework for MCP using fixed-point infinite impulse response and FIR filters. An efficient multiplication-free design of the algorithm that is suited for hardware implementation is shown. A detailed analysis of average and worst case complexities compared to FIR filter-based interpolation techniques is provided. Average bitrate savings of around 2.0% compared to an 8-tap FIR filter are observed over the high-efficiency video coding dataset at a similar worst case complexity.

The use of IIR (infinite impulse response) filters for edge detection is studied. IIR filters are shown to perform better than the FIR (finite impulse response) filters for this application. It is also shown that the computational savings in using IIR filters is quite significant.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Digital filters play a central role in modern Digital Signal Processing (DSP) systems. Finite Impulse Response (FIR) filters can provide solutions with guaranteed stability and linear phase. However, the main disadvantage of conventional FIR filter designs is that they become computationally complex, especially in applications demanding narrow transition bandwidths. Therefore, designing FIR filters with very stringent specifications and a low complexity is currently an important challenge. In this chapter, a review of the recent methods to efficiently design low-complexity linear-phase FIR filters is presented. The chapter starts with an introduction to linear-phase FIR digital filters. Then, an overview of the design methods that have been developed in literature to design low-complexity FIR filters is presented. Finally, the most common and recent of these methods along with their corresponding special structures are explained.