Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units
This work looks at the exploitation of large numbers of orthogonal redundant inertial measurement units. Specifically, the paper analyses centralized and distributed architectures in the context of data fusion algorithms for those sensors. For both architectures, data fusion algorithms based on Kalm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/6/1910 |
| _version_ | 1811184983375609856 |
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| author | Eric Gagnon Alexandre Vachon Yanick Beaudoin |
| author_facet | Eric Gagnon Alexandre Vachon Yanick Beaudoin |
| author_sort | Eric Gagnon |
| collection | DOAJ |
| description | This work looks at the exploitation of large numbers of orthogonal redundant inertial measurement units. Specifically, the paper analyses centralized and distributed architectures in the context of data fusion algorithms for those sensors. For both architectures, data fusion algorithms based on Kalman filter are developed. Some of those algorithms consider sensors location, whereas the others do not, but all estimate the sensors bias. A fault detection algorithm, based on residual analysis, is also proposed. Monte-Carlo simulations show better performance for the centralized architecture with an algorithm considering sensors location. Due to a better estimation of the sensors bias, the latter provides the most precise and accurate estimates and the best fault detection. However, it requires a much longer computational time. An analysis of the sensors bias correlation is also done. Based on the simulations, the biases correlation has a small effect on the attitude rate estimation, but a very significant one on the acceleration estimation. |
| first_indexed | 2024-04-11T13:21:43Z |
| format | Article |
| id | doaj.art-a6e93441d5e146829a154d94c6ee8596 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:21:43Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a6e93441d5e146829a154d94c6ee85962022-12-22T04:22:11ZengMDPI AGSensors1424-82202018-06-01186191010.3390/s18061910s18061910Data Fusion Architectures for Orthogonal Redundant Inertial Measurement UnitsEric Gagnon0Alexandre Vachon1Yanick Beaudoin2Defence Research and Development Canada, Quebec, QC G3J 1X5, CanadaNumérica Technologies Inc., Quebec, QC G2E 4P8, CanadaDépartement de génie électrique et génie informatique, Université Laval, Quebec, QC G1V 0A6, CanadaThis work looks at the exploitation of large numbers of orthogonal redundant inertial measurement units. Specifically, the paper analyses centralized and distributed architectures in the context of data fusion algorithms for those sensors. For both architectures, data fusion algorithms based on Kalman filter are developed. Some of those algorithms consider sensors location, whereas the others do not, but all estimate the sensors bias. A fault detection algorithm, based on residual analysis, is also proposed. Monte-Carlo simulations show better performance for the centralized architecture with an algorithm considering sensors location. Due to a better estimation of the sensors bias, the latter provides the most precise and accurate estimates and the best fault detection. However, it requires a much longer computational time. An analysis of the sensors bias correlation is also done. Based on the simulations, the biases correlation has a small effect on the attitude rate estimation, but a very significant one on the acceleration estimation.http://www.mdpi.com/1424-8220/18/6/1910orthogonal redundant inertial measurement unitsdata fusion architecturessensors bias |
| spellingShingle | Eric Gagnon Alexandre Vachon Yanick Beaudoin Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units Sensors orthogonal redundant inertial measurement units data fusion architectures sensors bias |
| title | Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units |
| title_full | Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units |
| title_fullStr | Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units |
| title_full_unstemmed | Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units |
| title_short | Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units |
| title_sort | data fusion architectures for orthogonal redundant inertial measurement units |
| topic | orthogonal redundant inertial measurement units data fusion architectures sensors bias |
| url | http://www.mdpi.com/1424-8220/18/6/1910 |
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