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A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integratio...

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Autores principales: Quinchia, Alex G., Falco, Gianluca, Falletti, Emanuela, Dovis, Fabio, Ferrer, Carles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812568/
https://www.ncbi.nlm.nih.gov/pubmed/23887084
http://dx.doi.org/10.3390/s130809549
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author Quinchia, Alex G.
Falco, Gianluca
Falletti, Emanuela
Dovis, Fabio
Ferrer, Carles
author_facet Quinchia, Alex G.
Falco, Gianluca
Falletti, Emanuela
Dovis, Fabio
Ferrer, Carles
author_sort Quinchia, Alex G.
collection PubMed
description Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.
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spelling pubmed-38125682013-10-30 A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems Quinchia, Alex G. Falco, Gianluca Falletti, Emanuela Dovis, Fabio Ferrer, Carles Sensors (Basel) Article Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways. Molecular Diversity Preservation International (MDPI) 2013-07-24 /pmc/articles/PMC3812568/ /pubmed/23887084 http://dx.doi.org/10.3390/s130809549 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Quinchia, Alex G.
Falco, Gianluca
Falletti, Emanuela
Dovis, Fabio
Ferrer, Carles
A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title_full A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title_fullStr A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title_full_unstemmed A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title_short A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems
title_sort comparison between different error modeling of mems applied to gps/ins integrated systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812568/
https://www.ncbi.nlm.nih.gov/pubmed/23887084
http://dx.doi.org/10.3390/s130809549
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