正四面体冗余惯导全参数现场快速标定方法

Full parameter rapid field calibration method for regular tetrahedral redundant inertial navigation

  • 摘要: 正四面体冗余惯导系统(RINS)具有高可靠性、高精度等特点,而误差标定是实现高精度导航解算的必要手段。当前正四面体RINS的误差标定均需要利用高精度转台实现,不仅标定成本高、标定时间长,而且在外场等硬件条件不足的情况下无法进行全误差参数的标定。针对这一问题,提出了一种无需高精度转台的正四面体RINS全误差参数现场快速标定方法。首先,建立了正四面体RINS的误差模型;然后,根据解析粗对准姿态误差矩阵与正四面体RINS零偏的关系提出了基于六位置的零偏标定方法;之后,设计三位置旋转方案进行陀螺仪的标度因数和安装误差标定;最后,利用零偏标定的六位置方案进行加速度计的标度因数和安装误差标定。仿真及试验结果表明,该方法能有效地标定出所有误差参数,在1 h静基座导航试验中,标定后北向定位精度从61.065 5 km提升至0.476 7 km,东向定位精度从161.202 7 km提升至4.842 2 km。

     

    Abstract: Regular tetrahedral Redundant Inertial Navigation System (RINS) has the characteristics of high reliability and high precision, and error calibration is a necessary means to realize high-precision navigation solution. At present, the error calibration of regular tetrahedral RINS needs to be realized by using the high-precision turntable, which not only has high calibration cost and long calibration time, but also cannot calibrate full error parameters under the condition of insufficient hardware conditions such as external field. Address this issue, a rapid field calibration method for full error parameters of regular tetrahedral RINS without high-precision turntable was proposed. Firstly, the error model of regular tetrahedral RINS was established. Then, according to the relationship between the analytic coarse alignment attitude error matrix and the bias of regular tetrahedral RINS, a bias calibration method based on six positions was proposed. Then, a three-position rotation scheme was designed to calibrate the scale factor and installation error of the gyroscope. Finally, the six-position scheme of bias calibration was used to calibrate the scale factor and installation error of the accelerometer. Simulation and experiment show that this method can effectively calibrate full error parameters. In the 1h static base navigation experiment, the north positioning accuracy are improved from 61.065 5 km to 0.476 7 km, and the east positioning accuracy are improved from 161.202 7 km to 4.842 2 km.

     

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