基于可观测性分析的旋转式惯导系统在线自标定方法的设计

本文建立了速度误差外观测量的静基座双轴旋转式惯导系统在线标定卡尔曼滤波模型，其状态向量包括地速误差、姿态失准角和惯性器件零偏、标度因数误差、安装误差，可估计旋转式惯导系统失准角与惯性器件误差参数。通过分段线性定常系统(PWCS)可观测性分析方法分析不同旋转方式下系统可观测性变化情况，得出双轴连续旋转的角运动方案可以改善卡尔曼滤波滤波的可观测性。根据基于奇异值分解的可观测度分析结果进行模型降阶，同时结合旋转式惯导系统的工程应用特性，得到12阶卡尔曼滤波参数模型。降阶系统阶数降低约55%，可以显著降低运算量，有效提高了导航计算机运算效率和实时性。仿真实验表明：降阶模型的估计精度不低于原模型，而且部分状态量的滤波收敛速度有提高。

Design of Observability Analysis Based On-line Self Calibration Method for Rotational INS

The stationary on-line calibration Kalman filter mathematical model of dual-axis rotary inertial navigation system (INS) whose observation is velocity errors is built. The state vector includes ground velocity errors, misalignment angles and the null bias, scale errors and installation errors of inertial instruments. The observability of the calibration system in different angular motion statuses is analyzed using piece-wise constant system (PWCS) method, which concludes that the method of dual-axis rotation can improve the observabilty of the calibration filter. The state vector is reduced according to the observabilty degree analysis of singular value decomposition (SVD) method and the application characteristics of the rotary INS, and the reduce model is 12-devision. The order number reduced model decreases 55%, which can notably cut down the calculation of Kalman filter and improve the efficiency and instantaneity of the navigation computer. Simulation indicates that the estimation precision is not lower than the original model and the filtering convergence of some state are accelerated.