{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T01:32:42Z","timestamp":1778376762648,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2013,11,15]],"date-time":"2013-11-15T00:00:00Z","timestamp":1384473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems.  Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation.<\/jats:p>","DOI":"10.3390\/s131115656","type":"journal-article","created":{"date-parts":[[2013,11,15]],"date-time":"2013-11-15T11:50:00Z","timestamp":1384516200000},"page":"15656-15672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Unscented Particle Filtering for Estimation of Shipboard Deformation Based on Inertial Measurement Units"],"prefix":"10.3390","volume":"13","author":[{"given":"Bo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automation and National Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Automation and National Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanqing","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Automation and National Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyin","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Automation and National Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6771","DOI":"10.3390\/s110706771","article-title":"Data fusion algorithms for multiple inertial measurement units","volume":"11","author":"Bancroft","year":"2011","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TITS.2011.2168818","article-title":"Inertial navigation aiding by stationary updates","volume":"13","author":"Ramanandan","year":"2012","journal-title":"IEEE Trans. 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