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Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors’ calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precisi...

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Detalles Bibliográficos
Autores principales: Nemec, Dusan, Andel, Jan, Simak, Vojtech, Hrbcek, Jozef
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385938/
https://www.ncbi.nlm.nih.gov/pubmed/37514726
http://dx.doi.org/10.3390/s23146431
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author Nemec, Dusan
Andel, Jan
Simak, Vojtech
Hrbcek, Jozef
author_facet Nemec, Dusan
Andel, Jan
Simak, Vojtech
Hrbcek, Jozef
author_sort Nemec, Dusan
collection PubMed
description The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors’ calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array.
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spelling pubmed-103859382023-07-30 Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors Nemec, Dusan Andel, Jan Simak, Vojtech Hrbcek, Jozef Sensors (Basel) Article The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors’ calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array. MDPI 2023-07-15 /pmc/articles/PMC10385938/ /pubmed/37514726 http://dx.doi.org/10.3390/s23146431 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nemec, Dusan
Andel, Jan
Simak, Vojtech
Hrbcek, Jozef
Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title_full Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title_fullStr Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title_full_unstemmed Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title_short Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors
title_sort homogeneous sensor fusion optimization for low-cost inertial sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385938/
https://www.ncbi.nlm.nih.gov/pubmed/37514726
http://dx.doi.org/10.3390/s23146431
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