Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking
The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordi...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1543 |
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| author | Yara Rossi Konstantinos Tatsis Mudathir Awadaljeed Konstantin Arbogast Eleni Chatzi Markus Rothacher John Clinton |
| author_facet | Yara Rossi Konstantinos Tatsis Mudathir Awadaljeed Konstantin Arbogast Eleni Chatzi Markus Rothacher John Clinton |
| author_sort | Yara Rossi |
| collection | DOAJ |
| description | The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring. In order to develop and validate this methodology, we have set up an experimental case study, relying on the use of an industrial six-axis robot arm, on which the instruments are mounted. The robot simulates the structural motion resulting atop a wind-excited wind turbine tower. The quality of the 6C KF reconstruction is assessed by comparing the estimated response to the feedback system of the robot, which performed the experiments. The fusion of rotational information yields significant improvement for both the acceleration recordings but also the GNSS positions, as evidenced via the substantial reduction of the RMSE, expressed as the difference between the KF predictions and robot feedback. This work puts forth, for the first time, a KF-based fusion for all six motion components, validated against a high-precision ground truth measurement. The proposed filter formulation is able to exploit the strengths of each instrument and recover more precise motion estimates that can be exploited for multiple purposes. |
| first_indexed | 2024-03-09T00:36:09Z |
| format | Article |
| id | doaj.art-161ff3002c424762ab01427d6e59b4d5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:36:09Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-161ff3002c424762ab01427d6e59b4d52023-12-11T18:07:01ZengMDPI AGSensors1424-82202021-02-01214154310.3390/s21041543Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion TrackingYara Rossi0Konstantinos Tatsis1Mudathir Awadaljeed2Konstantin Arbogast3Eleni Chatzi4Markus Rothacher5John Clinton6Institute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm Weg 15, CH-8093 Zurich, SwitzerlandInstitute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, CH-8093 Zurich, SwitzerlandInstitute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm Weg 15, CH-8093 Zurich, SwitzerlandInstitute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm Weg 15, CH-8093 Zurich, SwitzerlandInstitute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5, CH-8093 Zurich, SwitzerlandInstitute of Geodesy and Photogrammetry, ETH Zurich, Robert-Gnehm Weg 15, CH-8093 Zurich, SwitzerlandSwiss Seismological Service, ETH Zurich, Sonneggstrasse 5, CH-8092 Zurich, SwitzerlandThe ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring. In order to develop and validate this methodology, we have set up an experimental case study, relying on the use of an industrial six-axis robot arm, on which the instruments are mounted. The robot simulates the structural motion resulting atop a wind-excited wind turbine tower. The quality of the 6C KF reconstruction is assessed by comparing the estimated response to the feedback system of the robot, which performed the experiments. The fusion of rotational information yields significant improvement for both the acceleration recordings but also the GNSS positions, as evidenced via the substantial reduction of the RMSE, expressed as the difference between the KF predictions and robot feedback. This work puts forth, for the first time, a KF-based fusion for all six motion components, validated against a high-precision ground truth measurement. The proposed filter formulation is able to exploit the strengths of each instrument and recover more precise motion estimates that can be exploited for multiple purposes.https://www.mdpi.com/1424-8220/21/4/1543collocated vibration measurementsaccelerometerGNSSrotational sensorKalman filterdata fusion |
| spellingShingle | Yara Rossi Konstantinos Tatsis Mudathir Awadaljeed Konstantin Arbogast Eleni Chatzi Markus Rothacher John Clinton Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking Sensors collocated vibration measurements accelerometer GNSS rotational sensor Kalman filter data fusion |
| title | Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking |
| title_full | Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking |
| title_fullStr | Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking |
| title_full_unstemmed | Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking |
| title_short | Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking |
| title_sort | kalman filter based fusion of collocated acceleration gnss and rotation data for 6c motion tracking |
| topic | collocated vibration measurements accelerometer GNSS rotational sensor Kalman filter data fusion |
| url | https://www.mdpi.com/1424-8220/21/4/1543 |
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