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Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking

The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise...

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Detalles Bibliográficos
Autores principales: Hoang, Minh Long, Carratù, Marco, Paciello, Vincenzo, Pietrosanto, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304859/
https://www.ncbi.nlm.nih.gov/pubmed/37420768
http://dx.doi.org/10.3390/s23125603
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author Hoang, Minh Long
Carratù, Marco
Paciello, Vincenzo
Pietrosanto, Antonio
author_facet Hoang, Minh Long
Carratù, Marco
Paciello, Vincenzo
Pietrosanto, Antonio
author_sort Hoang, Minh Long
collection PubMed
description The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°.
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spelling pubmed-103048592023-06-29 Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking Hoang, Minh Long Carratù, Marco Paciello, Vincenzo Pietrosanto, Antonio Sensors (Basel) Article The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°. MDPI 2023-06-15 /pmc/articles/PMC10304859/ /pubmed/37420768 http://dx.doi.org/10.3390/s23125603 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
Hoang, Minh Long
Carratù, Marco
Paciello, Vincenzo
Pietrosanto, Antonio
Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title_full Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title_fullStr Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title_full_unstemmed Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title_short Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
title_sort fusion filters between the no motion no integration technique and kalman filter in noise optimization on a 6dof drone for orientation tracking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304859/
https://www.ncbi.nlm.nih.gov/pubmed/37420768
http://dx.doi.org/10.3390/s23125603
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