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Sensors 2018, 18(6), 1910;

Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units

Defence Research and Development Canada, Quebec, QC G3J 1X5, Canada
Numérica Technologies Inc., Quebec, QC G2E 4P8, Canada
Département de génie électrique et génie informatique, Université Laval, Quebec, QC G1V 0A6, Canada
Author to whom correspondence should be addressed.
Received: 18 May 2018 / Revised: 7 June 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
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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. View Full-Text
Keywords: orthogonal redundant inertial measurement units; data fusion architectures; sensors bias orthogonal redundant inertial measurement units; data fusion architectures; sensors bias

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Gagnon, E.; Vachon, A.; Beaudoin, Y. Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units. Sensors 2018, 18, 1910.

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