基于EPEA的SINS大失准角非线性初始对准方法

SINS nonlinear initial alignment methods for large misalignment angles based on EPEA

基于欧拉平台误差角(EPEA)的概念描述了理论导航坐标系到计算导航坐标系之间的失准角,推导了捷联惯导系统(SINS)在大失准角情况下进行初始对准的非线性误差模型.在系统噪声和量测噪声均为加性噪声且量测方程为线性方程时,给出了带阻尼解算的简化扩展卡尔曼滤波(EKF)算法和简化无迹卡尔曼滤波(UKF)算法,同时分析了不同失准角情况下初始对准过程的异同.静基座状态下的Monte Carlo仿真结果表明,大失准角和大方位失准角情况下,EKF和UKF算法都能满足对准要求,其中UKF算法较EKF算法具有对准时间更快、对准精度更高和适用范围更广的优点;小失准角情况下,由于捷联惯导系统的线性化误差变小,二者的对准时间和对准精度基本相同.

Euler platform error angles （EPEA） are adopted to describe the misalignment angles from the theoretical navigation coordinate system to the computational navigation coordinate system, and the strapdown inertial navigation system （SINS） nonlinear error model is derived for initial alignment at large initial misalignment angles. The simplified extended Kalman filter （EKF） and unscented Kalman filter （UKF） algorithms with damp solution are presented when both process noise and measurement noise are additive and the measurement equation is linear, and a comparison is made between the filtering processes for different misalignment angles. The Monte Carlo results from the stationary simulation show that both EKF and UKF algorithms can obtain satisfactory alignment accuracy under large and large azimuth misalignment angles. But UKF is superior in alignment time, alignment precision and application scope in most cases except for the case of small misalignment angles, and in such a situation they have the same alignment performance due to the fact that the linearization error of SINS becomes smaller.