Chaojun Liu, Shuai Yu, Shengzhi Zhang, Xuebing Yuan, Sheng Liu
{"title":"An effective unscented Kalman filter for state estimation of a gyro-free inertial measurement unit","authors":"Chaojun Liu, Shuai Yu, Shengzhi Zhang, Xuebing Yuan, Sheng Liu","doi":"10.1109/PLANS.2014.6851380","DOIUrl":null,"url":null,"abstract":"This study reports a gyro-free inertial measurement unit (IMU) using solely four triaxial accelerometers. System equations and a configuration which is feasible for the gyro-free IMU design are presented. The propagation of accelerometer measurement errors is analyzed. An unscented Kalman filter (UKF) is proposed for state estimation. Simulation results show that the system state is robustly estimated by the proposed UKF. Furthermore, compared with the results of error analysis, the UKF provides effective error reductions on state estimation. The error of angular velocity estimation over full scale (FS) is about ±0.4%FS.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"57 1464 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLANS.2014.6851380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This study reports a gyro-free inertial measurement unit (IMU) using solely four triaxial accelerometers. System equations and a configuration which is feasible for the gyro-free IMU design are presented. The propagation of accelerometer measurement errors is analyzed. An unscented Kalman filter (UKF) is proposed for state estimation. Simulation results show that the system state is robustly estimated by the proposed UKF. Furthermore, compared with the results of error analysis, the UKF provides effective error reductions on state estimation. The error of angular velocity estimation over full scale (FS) is about ±0.4%FS.