摘要
为提高BDS/GPS组合定位系统的精确性和稳定性,将高阶容积卡尔曼滤波(CKF)应用于定位参数估计,并利用矩阵对角化(DM)变换替代标准高阶CKF中的Cholesky分解过程。通过DM变换,由协方差矩阵分解得到的平方根矩阵具有状态统计量更加准确、保留原有特征空间信息的特点,从而提高了滤波精度;同时,DM变换的协方差矩阵不要求正定,增强了滤波的稳定性。测试结果表明,该滤波算法在提高定位精度和稳定性上有效、可行。
To improve the accuracy and stability of the BDS/GPS combined positioning system,the high-degree cubature Kalman filter( CKF) is applied to the estimation of the positioning parameters,and the diagonalization of matrix( DM) transformation is used to replace the Cholesky decomposition in the standard high degree CKF. Through the DM transformation,the square root matrix of the covariance matrix has more accurate state statistics and retains the characteristics of the original feature space information,and improves the filtering accuracy. The covariance matrix of DM transformation does not require positive definite,so the stability of filtering is enhanced. The test results show that the filtering algorithm is effective and feasible for improving the positioning accuracy and stability.
作者
张兆龙
王跃钢
腾红磊
张复建
刘海洋
ZHANG Zhao-long;WANG Yue-gang;TENG Hong-lei;ZHANG Fu-jian;LIU Hai-yang(The Rocket Force University of Engineering, Shaanxi Xi'an 710025 ,China)
出处
《现代防御技术》
2018年第3期48-53,92,共7页
Modern Defence Technology
基金
国防自然科学基金(61304001)
关键词
半和改正模型
定位算法
非差分模型
高阶容积卡尔曼滤波
矩阵对角化变换
定位精度
semi-correcting model
positioning algorithm
non-differential model
high-degree cuba-ture Kalman filter (HCKF)
matrix diagonalization
positioning accuracy