GOCE SGG filtering with FIR, IIR and wavelet MRA  

2021 ◽  
Author(s):  
Eleftherios A. Pitenis ◽  
Elisavet G. Mamagiannou ◽  
Dimitrios A. Natsiopoulos ◽  
Georgios S. Vergos ◽  
Ilias N. Tziavos
Keyword(s):  
Gravity Field ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Gravity Gradient ◽  
Satellite Gravity ◽  
Iir Filters ◽  
Field Spectrum ◽  
Lower Accuracy ◽  
Goce Satellite

<p>GOCE Satellite Gravity Gradiometry (SGG) data have been widely used in gravity field research in order to provide improved representations of the gravity field spectrum either in the form of Global Geopotential Models (GGMs) or grids at satellite altitude. One of the key points in utilizing SGG observations is their proper filtering, in order to remove noise and long-wavelength correlated error, while the signals in the GOCE measurement bandwidth (MBW) should be preserved. Due to the gradiometer’s design, the GOCE satellite can achieve high accuracy and stable measurements in the MBW of 0.005 Hz to 0.1 Hz. The gravity gradient in MBW are at an equivalent accuracy level, while   are of lower accuracy. Outside of the MBW, systematic errors, colored noise, and noise with sharp peaks are observed, especially in the frequencies lower than 0.005 Hz. With that in mind, the present work focuses on the investigation of various filtering options ranging from Finite Impulse Response (FIR) filters, Infinite Impulse Response (IIR) filters, and filtering based on Wavelets. The latter are employed given their inherent characteristic of being localized both in frequency and space, meaning that the signal can be decomposed at different levels, thus allowing multi-resolution approximation (MRA). The analysis is performed with one month of GOCE SGG data in order to conclude on the method that provides the overall best results. SGG observations are reduced to a GGM in order to account for the long- and medium-wavelengths of the gravity field spectrum. Then, various filter orders are investigated for the FIR and IIR filters, while selective reconstruction is employed for the WL-MRA. Evaluation of the results is performed in terms of the smoothness of the filtered fields and the Power Spectral Density (PSD) functions of the entire GOCE tensor.</p>

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).

GeoGravGOCE: A GOCE SGG processing software for datum transformations and filtering

2021 ◽  
Author(s):  
Elisavet Maria G. Mamagiannou ◽  
Eleftherios A. Pitenis ◽  
Dimitrios A. Natsiopoulos ◽  
Georgios S. Vergos ◽  
Ilias N. Tziavos
Keyword(s):  
Reference Frame ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Data Consistency ◽  
Infinite Impulse Response ◽  
Geopotential Model ◽  
Gravity Gradients ◽  
Satellite Gravity ◽  
Multi Resolution Analysis ◽  
Quality Checks

<p>Whilst GOCE SGG data have been widely processed and used in geodetic research, one of the key points of their use is to have a one-stop software for their pre-processing and basic manipulations in terms of frame transformations and filtering operations. Within the GeoGravGOCE project, funded by the Hellenic Foundation for Research Innovation, the main goal is the optimal combination of GOCE Satellite Gravity Gradiometry (SGG) data with in-situ observations for geoid determination. During the project development, it became apparent that GOCE SGG data after using the GOCEPARSER, had to be pre- and post-processed via several own-developed routines in order to perform data quality checks, data consistency tests, reference frame transformations, data reductions and filtering. With that in mind, a standalone open-source software has been developed in MATLAB consisting of a Graphical User Interface (GUI) to perform the aforementioned operation. The software is divided in four tabs and is designed to process the original GOCE gravity gradients, which are the second-order derivatives of the gravitational potential. The first tab of the software is designed to allow the pre-processing of the Level 2 Electrostatic Gravity Gradiometer nominal gravity gradients (EGG_NOM) and Satellite to Satellite Tracking Precise Science Orbits (SST_PSO) products. The second tab enables the transformation of gravity gradients from a Global Geopotential Model (GGM) from the Local North Oriented Frame (LNOF) to the Gradiometer Reference Frame (GRF). The third tab provides filtering options for the reduced SGG observations and encompasses three different methods: Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Wavelet Multi-Resolution Analysis (WL-MRA). Finally, the fourth tab allows the transformation of SGG data from the GRF to the LNOF and vice versa. In this work, we present the basic software development procedure and outline its basic functionality and results.</p>

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.

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.

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.

scholarly journals NANO-studio, the design environment of filter banks implemented in standard CMOS technology

2021 ◽  
Author(s):  
Andrzej Handkiewicz ◽  
Mariusz Naumowicz
Keyword(s):  
Impulse Response ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Cmos Process ◽  
Digital Cmos ◽  
Additional Advantage ◽  
Analysis And Synthesis ◽  
Optimization Parameters ◽  
Synthesis Filter

AbstractThe paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

Comparison of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) Filters in Cardiac Optical Mapping Records

2021 ◽  
pp. 207-224
Author(s):  
David Rivas-Lalaleo ◽  
Sergio Muñoz-Romero ◽  
Monica Huerta ◽  
Víctor Bautista-Naranjo ◽  
Jorge García-Quintanilla ◽  
...  
Keyword(s):  
Impulse Response ◽  
Optical Mapping ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Fir Filters

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.

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