基于SRCKF的水下航行器动基座初始对准技术  

Initial alignment method of the dynamic base for underwater vehicles using SRCKF

作　　者：郝燕玲[1] 杨峻巍 陈亮[1] 郝金会[1] 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《华中科技大学学报（自然科学版）》2012年第2期123-127,共5页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(60674087)

摘　　要：针对当海况不佳时,水下航行器即使处于系泊状态下也会产生大幅晃动,从而使得捷联惯性导航系统(SINS)无法快速完成自主初始对准的问题,提出了利用多普勒计程仪(DVL)提供的速度信息来辅助SINS实现运动中对准的方法.同时针对该环境下SINS只能获取粗略的初始方位信息的问题,提出了基于平方根求容积卡尔曼滤波(SRCKF)的动基座对准滤波算法.该滤波算法克服了扩展卡尔曼滤波(EKF)存在的滤波精度低以及须计算雅可比矩阵的不足,并在滤波过程中以协方差平方根矩阵代替协方差矩阵进行迭代更新,从而提高了滤波算法的收敛速度和数值稳定性.仿真结果表明:该方法能够解决水下航行器的动基座初始对准问题,且对准精度高,数值稳定性好.The initial alignment of strapdown inertial navigation system （SINS） can not be achieved quickly in moorage when the ocean condition is terrible. So the velocity information from Doppler velocity log（DVL） was used to help implement the alignment in the motion. Meanwhile, the rough azi-muth misalignment was only obtained in such environment, so the square root cubature Kalman filter （SRCKF） was put forward for initial alignment on the dynamic base. The filter algorithm effectively solved the problem of low precision and calculated the Jacobin matrix in extended Kalman filter. Fur-thermore, covariance square root matrix, instead of covariance one, was taken in filter iteration, which improved the convergence rate and stability of the algorithm. The simulation results show that the method can be effectively used in the initial alignment on dynamic base for underwater vehicles, and improve the alignment accuracy and numerical stability.

关 键 词：动基座 初始对准 求容积卡尔曼滤波 扩展卡尔曼滤波 惯性导航系统 

分 类 号：V249.32[航空宇航科学与技术—飞行器设计]