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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5603 |
| _version_ | 1797592659440697344 |
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| author | Minh Long Hoang Marco Carratù Vincenzo Paciello Antonio Pietrosanto |
| author_facet | Minh Long Hoang Marco Carratù Vincenzo Paciello Antonio Pietrosanto |
| author_sort | Minh Long Hoang |
| collection | DOAJ |
| 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°. |
| first_indexed | 2024-03-11T01:57:17Z |
| format | Article |
| id | doaj.art-afaea412506740d681867b832898b443 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:17Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-afaea412506740d681867b832898b4432023-11-18T12:33:25ZengMDPI AGSensors1424-82202023-06-012312560310.3390/s23125603Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation TrackingMinh Long Hoang0Marco Carratù1Vincenzo Paciello2Antonio Pietrosanto3Department of Engineering and Architecture, University of Parma, 43124 Parma, PR, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, ItalyThe 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°.https://www.mdpi.com/1424-8220/23/12/5603orientation trackingIMUMEMSdroneKalman filterno motion no integration filter |
| spellingShingle | Minh Long Hoang Marco Carratù Vincenzo Paciello Antonio Pietrosanto Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking Sensors orientation tracking IMU MEMS drone Kalman filter no motion no integration filter |
| 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 | orientation tracking IMU MEMS drone Kalman filter no motion no integration filter |
| url | https://www.mdpi.com/1424-8220/23/12/5603 |
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