FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Aladin Kapić ◽  
Rijad Sarić ◽  
Slobodan Lubura ◽  
Dejan Jokić
Keyword(s):  
Transfer Function ◽  
Impulse Response ◽  
Discrete Time ◽  
Time Domain ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Role ◽  
Digital Implementation ◽  
Iir Filter

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

2016 ◽  
Vol 24 (6) ◽  
pp. 1086-1100
Author(s):  
Utku Boz ◽  
Ipek Basdogan
Keyword(s):  
Computational Complexity ◽  
Vibration Control ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Active Vibration

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

The Spectra of Filters

2021 ◽  
pp. 204-268
Author(s):  
Victor Lazzarini
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Discrete Time ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Body ◽  
Analysis Tool ◽  
The Fourier Transform ◽  
Definition Of

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.

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).

Theory of the Design, and Operation of Digital Filters

2021 ◽  
pp. 797-823
Author(s):  
Stevan Berber
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Transformation Method ◽  
Infinite Impulse Response ◽  
Bilinear Transformation ◽  
Finite Impulse Response Filters ◽  
Infinite Impulse Response Filters ◽  
Basic Characteristics ◽  
Impulse Response Filters

Chapter 16 present the theoretical basis for digital filters, including issues related to their design. The basic characteristics and structures of finite impulse response and infinite impulse response filters are presented and discussed. In addition, the ideal and practical transfer characteristics of the digital filters are defined. The basic advantage of finite impulse response filters is that they can be designed to have an exact linear phase. However, infinite impulse response filters are generally more efficient computationally. The methods for filters design and related algorithms, which are based on the bilinear transformation method, windowed Fourier series, and algorithms based on iterative optimization, are also presented.

scholarly journals Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1468
Author(s):  
Xiang An ◽  
George K. Stylios
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Median Filter ◽  
Infinite Impulse Response ◽  
Ecg Signal ◽  
Motion Artefact ◽  
Iir Filter ◽  
Adaptive Motion ◽  
Artefact Reduction

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

Research of Sea Clutter Suppression Algorithm Based on GPU

2012 ◽  
Vol 239-240 ◽  
pp. 1194-1201
Author(s):  
Yan Guo ◽  
Shi Dan Li ◽  
De Sheng Wang
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Graphics Processing Unit ◽  
Main Idea ◽  
Fir Filter ◽  
Infinite Impulse Response ◽  
Processing Unit ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Iir Filter

This paper presents an algorithm of sea clutter suppression using graphics processing unit (GPU) to meet the real-time requirement in the general radar terminal system. The main idea is to convert an infinite impulse response (IIR) filter to a finite impulse response (FIR) filter, which is suitable for the parallelization processing of GPU. Finally, the converted FIR filter algorithm is implemented on the GPU efficiently, achieving a speed approximately twice as fast as that of the previous IIR filter algorithm implemented on the CPU.

scholarly journals A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1523
Author(s):  
Cornelis Jan Kikkert
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Block Diagram ◽  
Rate Of Change ◽  
Measurement Unit ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Phasor Measurement ◽  
Ieee Standard

Phasor measurement units (PMU) are increasingly used in electrical power transmission networks, to maintain stability and protect the network. PMUs accurately measure voltage, phase, frequency, and rate of change of frequency (ROCOF). For reliability, it is desirable to implement a PMU using an FPGA. This paper describes a novel algorithm, suited to implementation in an FPGA and based on a simple PMU block diagram. A description of its realization using low hardware complexity infinite impulse response (IIR) filters is given. The IEC/IEEE standard 60255-118-1:2018 Part 118-1: Synchrophasor measurements for power systems, describes “reference” Finite Impulse Response (FIR) filters for implementing PMU hardware. At the 10 kHz sampling frequency used for our implementation, each “reference” FIR filter requires 100 multipliers, while an 8th order IIR filter only requires 12 multipliers. This paper compares the performance of different order IIR filter-based PMUs with the performance of the same PMU algorithm using the IEC/IEEE FIR reference filter. The IIR-based PMU easily satisfies all the requirements of IEC/IEEE standard and has a much better out of band signal rejection performance than a FIR-based PMU. Steady state errors for a rated voltage ± 10% and a rated frequency ± 5 Hz are < 0.000001% for total vector error (TVE) and < 1 µHz for frequency, with a latency of two mains cycles.

Vibration Suppression Based on Adaptive Feedforward Control With Infinite Impulse Response Filter

2015 ◽  
Author(s):  
Shiying Zhou ◽  
Masayoshi Tomizuka
Keyword(s):  
Impulse Response ◽  
Vibration Suppression ◽  
Finite Impulse Response ◽  
Feedforward Control ◽  
Vibration Signal ◽  
Infinite Impulse Response ◽  
Least Mean Square ◽  
Parameter Adaptation ◽  
Iir Filter ◽  
Adaptive Feedforward

This paper presents adaptive feedforward control for vibration suppression based on an infinite impulse response (IIR) filter structure. The vibration signal and the output signal are available for the algorithm to adaptively update the parameters of the vibration transmission path (VTP) dynamics. Two designs for parameter adaptation are proposed. They provide different methods to get the necessary signals for parameter adaptation of the IIR filter which is different from the conventional finite impulse response (FIR) filter adaptation design. Performance of the proposed designs is compared with the conventional Filtered-x Least Mean Square (FxLMS) method on a hard disk drive (HDD) benchmark problem. The simulation results show that the proposed designs have smaller 3σ value and peak to peak value at steady state.

scholarly journals On Using a Genetic Algorithm in the Designing of Linear Digital Filters

2020 ◽  
Vol 1 (346) ◽  
pp. 113-124
Author(s):  
Jacek Stelmach
Keyword(s):  
Genetic Algorithm ◽  
Impulse Response ◽  
Digital Filters ◽  
Noise Suppression ◽  
Transfer Functions ◽  
Finite Impulse Response ◽  
Optimal Parameter ◽  
Moving Average ◽  
Infinite Impulse Response ◽  
Input Signals

Digital filters, either as filters with moving average (Finite Impulse Response) or autoregressive filters (Infinite Impulse Response), are widely used in noise suppression, signal processing or extracting information from data streams. Although well‑known theory allows for optimal parameter selection, there still exist such real applications where requirements limit the use of digital filters. One of the most important limitations is the response time delay caused by too many used lagged input signals. The method proposed in the article allows us to estimate filter parameters with a genetic algorithm, decreasing its delay but keeping the requirements important for the user (e.g.: attenuation). Transfer functions of such filters were compared with transfer functions of the most known classical filters.

scholarly journals DIRECT SYNTHESIS OF LADDER WAVE DIGITAL FILTERS WITH TUNABLE PARAMETERS

2017 ◽  
Vol 20 (1) ◽  
pp. 1-18
Author(s):  
S.A. Samad
Keyword(s):  
Transfer Function ◽  
Digital Filters ◽  
Direct Method ◽  
Direct Synthesis ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Minimum Number ◽  
Wave Digital Filters ◽  
Domain Transfer ◽  
Bandstop Filters

This paper proposes a method for the synthesis of ladder wave digital filters (WDFs) directly from the digital domain. This method avoids the need for the synthesis of analog reference filters conventionally required in WDF design. This direct method allows for the determination of the WDF coefficients from the digital domain transfer function. This is similar to conventional infinite impulse response (IIR) filter coefficient determination but the WDF will give a more efficient realization. Due to the WDFs power complementary properties, a first-order ladder WDF can simultaneously realize both lowpass and highpass responses using the same structure, while a second-order WDF can realize both the bandpass and bandstop responses simultaneously. By appropriately choosing the WDF adaptor configuration and structure, tunable parameters can be determined from the digital domain transfer function that controls the 3dB cut-off frequency of the lowpass and highpass filters, and the centre frequency and 3-dB bandwidth of the bandpass and bandstop filters. This results in the WDFs requiring a minimum number of multipliers for realization.

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