校准
惯性测量装置
计算机科学
卡尔曼滤波器
卫星
惯性导航系统
旋转(数学)
轨道(动力学)
扩展卡尔曼滤波器
航天器
传感器融合
遥感
控制理论(社会学)
惯性参考系
计算机视觉
工程类
航空航天工程
人工智能
数学
物理
地理
统计
量子力学
控制(管理)
作者
Jiazhen Lu,Maoqing Hu,Yanqiang Yang,Minpeng Dai
标识
DOI:10.1109/jsen.2020.2965136
摘要
Aiming at the long term and high-precision performance maintenance requirements of the satellite-borne four-axis redundant inertial measurement unit (IMU) in space, the satellite navigation velocity information, the attitude information provided by the star sensor and the redundant measurement output are used as references to derive the measurement model, including the star sensor installation error. Three rotation sequences of optimal observation, sub-optimal observation, and under-observation are designed and compared. The Kalman filter is used to calibrate the redundant IMU in the orbit. The numerical simulation shows that all three rotation sequences can calibrate more than 91% of the constant error, which meets the requirements of the use, and the calibration accuracy of the optimal rotation sequence is the highest. The ground repeatability calibration experiment is carried out 10 times for the optimal rotation sequence. The calibration results show that the range of all the error terms is less than its threshold, illustrating that the calibration result is stable and reliable. It is a real-time calibration method for all error terms on orbit, providing a reference method for the high-precision performance maintenance of the spacecraft integrated navigation system.
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