基于KF/UKF组合滤波的SINS大方位失准角初始对准

KF /UKF Based SINS Initial Alignment under Large Azimuth Misalignment Angle

为了解决无损卡尔曼滤波(UKF)算法计算量随着状态维数增加而急剧增大的问题,将非线性初始对准滤波模型分解为线性与非线性两部分,并提出一种基于此模型分解的KF/UKF组合滤波算法,设计了该组合算法的滤波步骤,理论上证明了该组合算法仍为最小均方误差估计意义下的最优估计。通过仿真实验比较了KF/UKF组合算法和UKF算法的滤波效果,结果表明,基于KF/UKF组合算法的运算速度优于UKF算法。

In order to solve the problem that the calculation complexity increase sharply with the state dimension increment, the KF/UKF algorithm is proposed by decomposing the initial alignment model into linear part and nonlinear part. In this paper, the filtering process is designed and the algorithm is theoretically proved to be the optimal estimation with the minimum mean square error. At last, the KF/UKF algorithm is compared with the traditional UKF algorithm by use of the simulation experiment. The results show that the KF/UKF algorithm is better both initial alignment accuracy and computational speed.