降维CKF算法及其在SINS初始对准中的应用  

Reduced dimension CKF algorithm and its application in SINS initial alignment

作　　者：钱华明[1] 葛磊[1] 黄蔚[1] 彭宇 

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：《系统工程与电子技术》2013年第7期1492-1497,共6页Systems Engineering and Electronics

基　　金：国家自然科学基金(61104036)资助课题

摘　　要：针对常规容积卡尔曼滤波(cubature Kalman filter,CKF)算法在捷联惯导系统(strapdown inertialnavigation system,SINS)大方位失准角初始对准中采样点个数与状态向量维数成正比、计算量较大的问题,提出了降维CKF算法。与常规CKF算法相比,该算法只对离散化后的SINS非线性误差模型中的大方位失准角进行采样,再利用三阶球面-相径容积规则计算后验均值和协方差,从而将采样向量从10维降低到1维,采样点数量从20个下降到2个,减小了计算量。仿真实验结果表明,该算法与常规CKF算法具有相同的对准精度,计算时间仅为常规CKF算法的1/3,是一种较为实用的方法。According to the fact that when a conventional cubature Kalman filter（CKF） is adopted for strapdown inertial navigation system（SINS） initial alignment with large azimuth misalignment,the sampling points are directly proportional to the dimension of state vector,and the calculation amount is large,a reduced dimension CKF algorithm is proposed.Comparing with the conventional CKF algorithm,only the large azimuth misalignment angle is sampled in the discreted SINS nonlinear error model,and the third-degree spherical-radial cubature rule is used to calculate the posterior mean and covariance.The new approach reduces the sampling vector from 10 dimension to 1 dimension,and reduces the sampling points from 20 to 2,which reduces the calculation amount.The simulation shows that new approach has the same alignment accuracy as the conventional CKF algorithm,while the computational time is reduced to 1/3 of the conventional CKF algorithm,which proves the practicality of the new approach.

关 键 词：捷联惯导系统 初始对准 常规容积卡尔曼滤波算法 降维容积卡尔曼滤波算法 三阶球面-相径容积规则 

分 类 号：U666.1[交通运输工程—船舶及航道工程]