摘要
针对大方位失准角捷联惯性导航系统误差模型非线性的特点,利用基于迭代测量更新的中心差分卡尔曼滤波(iterated central difference Kalman filter,ICDKF)方法进行初始对准。与传统的非线性扩展卡尔曼滤波相比,ICDKF不仅能够提高滤波精度,而且不需要模型的具体解析形式,避免了复杂的雅可比矩阵的推导;同时ICDKF通过迭代测量更新,提高了目前存在的中心差分卡尔曼滤波的估计精度。仿真结果进一步表明ICDKF算法的可行性与优越性,能够满足初始对准的要求。
In case of that the error model of strapdown inertial navigation system with large azimuth mis- alignment angle is nonlinear, an iterated central difference Kalman filter （ICDKF） is used in initial alignment. Compared with the traditional extended Kalman filter, ICDKF not only improves filter precision but also avoids calculating the complicated 3acobian matrix. And ICDKF can improve the estimation accuracy of existing central difference Kalman filter with iterated measurement updating procedure. The simulation results further demon- strate the feasibility and superiority of ICDKF, which the requirement of initial alignment can be met.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2013年第1期152-155,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60674087)资助课题
关键词
初始对准
捷联惯导系统
迭代中心差分卡尔曼滤波
大方位失准角
initial alignment
strapdown inertial navigation system （SINS）
iterative central difference Kal- man filter （ICDKF）
large azimuth misalignment angle
