旋转式SINS的自标校技术研究

惯性器件常值及慢变误差是影响捷联惯导系统精度的主要因素之一,所以在捷联惯导系统出厂前需要对常值及慢变误差参数进行标定。但这些误差参数会随时间发生变化,对于高精度捷联惯导系统,每次启动后需要对惯性器件的误差参数进行重新标校。针对光纤惯导系统,建立了IMU误差模型,并根据提出的旋转式捷联惯导系统自标校转位方案原则设计出了一种8位置自标校方案,对惯性器件标定参数进行激励和辨识,并建立了Kalman滤波状态方程及量测方程,对惯导系统误差参数进行在线标定。实验结果表明,该方案对其惯性器件误差参数能进行准确估计,具有一定的参考价值。

Research on the Self -alibration Technique of Rotating SINS

No matter it is constant or slow varying, the bias of the inertial sensors is one of the main factors that influence the precision of the strapdown inertial navigation system (SINS), which makes it necessary for the bias of the sensors to be calibrated before used. And for high-precision SINS, as these errors change over time, the parameters of inertial devices need to be re-calibrated each time the SINS operates. For the fiber-optic inertial navigation system, the IMU error model is established and an eight-position self-calibration scheme is designed according to the proposed principle of self-calibration for the SINS. The inertial sensors calibration parameters are excited and identified, the Kalman filter state equation and measurement equation are established, and the online calibration of the error parameters for the inertial navigation system is conducted. Experimental results show that the proposed scheme can accurately estimate the error of inertial sensors and provide certain reference value.