基于5阶降维平方根-容积卡尔曼滤波的动基座对准应用研究

为提高动基座下捷联惯导系统的对准精度、数值稳定性和减小计算量,将5阶容积卡尔曼滤波(CKF)、降维算法、多次离散和平方根(SR)滤波结合起来,形成5阶降维SR-CKF非线性对准方案。为减小5阶CKF的计算量,建立非线性-线性分离的系统模型,引入降维算法;为提高1阶龙格-库塔法的逼近精度,设计多次离散和时间更新的滤波框架;为提高数值稳定性,推导了5阶降维SR-CKF;比较常规3阶SR-CKF、5阶CKF和5阶降维SR-CKF的各项特性。实车动基座对准实验结果表明:该方案对准精度高、数值稳定性强、计算量小,满足应用需要。

Research on Initial Alignment of Moving Base with 5th-degree Dimensionality Reduction SR-CKF

In order to achieve higher alignment precision, stronger numerical stability and lower computational cost for nonlinear alignment of strapdown inertial navigation system (SINS) on moving base, a scheme of 5th-degree dimensionality reduction SR-CKF nonlinear alignment is proposed，which combines 5th-degree cubature Kalman filter (CKF), dimensionality reduction algorithm, multiple discretization, and square root(SR) filter. A nonlinear-linear separation system model is established, and the dimensionality reduction algorithm is introduced to reduce the calculated amount. A multiple discretization and time update filter framework is designed to improve the approximation accuracy. The 5th-degree dimensionality reduction SR-CKF is deduced to improve the numerical stability. The features of the conventional 3rd-degree SR-CKF, 5th-degree CKF and the proposed algorithm are compared. The experimental results show that the proposed method has a high alignment precision, strong numerical stability and little calculated amount, which meets the application requirements.