{"title":"FOG-IMU混合分级定标方法及其实验验证","authors":"J. Chang, Bo Zhao, Fei Yu, R. Zhang, P. Wu","doi":"10.1109/INERTIALSENSORS.2017.8171498","DOIUrl":null,"url":null,"abstract":"There exist some problems in the fiber optic gyro inertial measurement unit(FOG-IMU) calibration, such as dependence on high-precision turntable, low precision, rigorous calibration condition and so on. Aiming at these problems, we propose a high-precision grading calibration method. Firstly, system parameters are calibrated by the discrete calibration method, in which the effects of the lever-arm are considered. Secondly, sensors' parameters are compensated effectively. Afterwards, the residual components of the IMU parameters are used as estimated objects and a 27-dimension Kalman filter is established by using velocity errors as observed quantities. The residual errors of coarse calibration will be estimated and compensated as well. Finally, results are validated through turntable experiments.","PeriodicalId":402172,"journal":{"name":"2017 DGON Inertial Sensors and Systems (ISS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid grading calibration method of FOG-IMU and its experimental verification\",\"authors\":\"J. Chang, Bo Zhao, Fei Yu, R. Zhang, P. Wu\",\"doi\":\"10.1109/INERTIALSENSORS.2017.8171498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There exist some problems in the fiber optic gyro inertial measurement unit(FOG-IMU) calibration, such as dependence on high-precision turntable, low precision, rigorous calibration condition and so on. Aiming at these problems, we propose a high-precision grading calibration method. Firstly, system parameters are calibrated by the discrete calibration method, in which the effects of the lever-arm are considered. Secondly, sensors' parameters are compensated effectively. Afterwards, the residual components of the IMU parameters are used as estimated objects and a 27-dimension Kalman filter is established by using velocity errors as observed quantities. The residual errors of coarse calibration will be estimated and compensated as well. Finally, results are validated through turntable experiments.\",\"PeriodicalId\":402172,\"journal\":{\"name\":\"2017 DGON Inertial Sensors and Systems (ISS)\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 DGON Inertial Sensors and Systems (ISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INERTIALSENSORS.2017.8171498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 DGON Inertial Sensors and Systems (ISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INERTIALSENSORS.2017.8171498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hybrid grading calibration method of FOG-IMU and its experimental verification
There exist some problems in the fiber optic gyro inertial measurement unit(FOG-IMU) calibration, such as dependence on high-precision turntable, low precision, rigorous calibration condition and so on. Aiming at these problems, we propose a high-precision grading calibration method. Firstly, system parameters are calibrated by the discrete calibration method, in which the effects of the lever-arm are considered. Secondly, sensors' parameters are compensated effectively. Afterwards, the residual components of the IMU parameters are used as estimated objects and a 27-dimension Kalman filter is established by using velocity errors as observed quantities. The residual errors of coarse calibration will be estimated and compensated as well. Finally, results are validated through turntable experiments.