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040 |aCaOODSP|beng|erda|cCaOODSP
0861 |aD68-10/083-2018E-PDF
1001 |aGagnon, Eric, |eauthor.
24510|aData fusion architectures for orthogonal redundant inertial measurement units / |cEric Gagnon, DRDC - Valcartier Research Centre ; Alexandre Vachon, Numérica Technologies Inc. ; Yanick Beaudoin, Université Laval.
264 1|aQuebec (Quebec) : |bDefence Research and Development Canada = Recherche et développement pour la défense Canada, |c2018.
264 4|c©2018
300 |a1 online resource (20 pages, 2 unnumbered pages) : |bcolour illustrations.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |aExternal literature (P) ; |vDRDC-RDDC-2018-P083
500 |a"Can unclassified."
500 |a"Sensors, MDPI, Sensors 2018, 18(6), 1910."
500 |a"July 2018."
504 |aIncludes bibliographical references (pages 19-20).
5203 |a"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"--Abstract.
7102 |aDefence R&D Canada.
7101 |aDefence R&D Canada. |bValcartier Research Centre.
830#0|aExternal literature (P) (Defence R&D Canada)|vDRDC-RDDC-2018-P083.|w(CaOODSP)9.854437
85640|qPDF|s1.84 MB|uhttps://publications.gc.ca/collections/collection_2019/rddc-drdc/D68-10-083-2018-eng.pdf