{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:22:45Z","timestamp":1760242965412,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,2,27]],"date-time":"2015-02-27T00:00:00Z","timestamp":1424995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage                                                    and temperature     as the input variables and angular velocity error     as the output variable. Firstly, the angular velocity error     is extracted from OFOG output signals, and then the output voltage    , temperature     and angular velocity error     are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T,     and     is established and thus     can be calculated automatically to compensate OFOG errors according to     and    . The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by    ,     and     relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by    ,     and    , respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.<\/jats:p>","DOI":"10.3390\/s150304899","type":"journal-article","created":{"date-parts":[[2015,2,27]],"date-time":"2015-02-27T10:08:39Z","timestamp":1425031719000},"page":"4899-4912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling"],"prefix":"10.3390","volume":"15","author":[{"given":"Yanshun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University,  Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University,  Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University,  Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University,  Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanqing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1364\/AO.51.002541","article-title":"Analysis and modeling for fiber-optic gyroscope scale factor based on environment temperature","volume":"51","author":"Shen","year":"2012","journal-title":"Appl. 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