混合式光纤陀螺惯导系统在线自主标定

Online self-calibration of hybrid FOG inertial navigation system

混合式光纤陀螺惯导系统IMU的安装误差、光纤陀螺的漂移及标度因数等参数会随着时间发生变化,对系统误差产生影响,使系统在使用一段时间之后精度发生变化,因而需要重新标定。在混合式系统中,通过台体旋转调制,惯性元件常值漂移误差对系统的影响得到抑制,但安装误差和标度因数误差对系统的影响无法得到完全调制,这些误差会与地速及旋转角速率耦合,引起锯齿形速度误差,降低了系统的各项性能。针对混合式惯导系统,建立了IMU误差模型,设计出一种在线自主标定方法,并进行了可观性分析。该方法采用"速度+位置"匹配,对惯导系统30项相关误差项进行在线标定。系统实验结果表明,系统级在线标定参数较分立式标定参数在导航定位精度上提高了半个数量级。

The hybrid FOG inertial navigation system must be periodically recalibrated to overcome its various accumulated errors. Unlike the inertial elements＇ constant drift errors, the mounting error and scale factor error can not be restrained by the platform＇s rotary modulation, and these errors would be coupled with the Earth＇s rotational rate and the platform＇s rotational velocity, which would induce the sawtooth speed errors, etc, and then reduce the performances of the system. To solve this problem and improve the system＇s accuracy, an IMU error model is established for the inertial navigation system, and an on-line autonomous calibration method is designed, which uses ＂velocity ＋ position＂ matching Kalman filter scheme to online calibrate 30 items of relative error terms of inertial navigation system. This method uses its own indexing mechanism to implement system-level calibration, and its error parameters are made converge by implementing navigation calculations, Kalman filtering, and multiple iterations. The error propagation characteristics of the system are analyzed, and the observability analysis is carried out. Experimental results show that the 90 h navigation positioning accuracy by the proposed system-level online calibration method is increased by half an order of magnitude than that by traditional discrete calibration method.