计算机工程与应用Issue(10)：225-229,240,6.DOI:10.3778/j.issn.1002-8331.1511-0335
改进的强跟踪SVD-UKF算法在组合导航中的应用
Exploration of improved strong tracking SVD-UKF used in BDS/INS integrated navigation
摘要
Abstract
The performance of the Unscented Kalman filter would be degraded in accuracy or divergences when the sys-tem states are uncertain and strong nonlinear, an improved strong tracking SVD-UKF algorithm is proposed. The iteration of covariance matrix in UKF is improved by Singular Value Decomposition(SVD)of covariance matrix, ensured the sta-bility of the iteration of covariance matrix and restrained the negative definiteness of system state covariance matrix. Mul-tiple fading factors matrices are introduced in improved SVD-UKF, in order to automatic improve system state covariance matrix based on Strong Tracking Filter(STF)theory framework, and realize the strong tracking of the real state while sys-tem status are mutating. The proposed strong tracking SVD-UKF is applied to the BDS/INS integrated system for simula-tion, simulation results show the effectiveness of the presented algorithm.关键词
无迹卡尔曼滤波(UKF)/奇异值分解(SVD)/强跟踪/渐消因子/组合导航Key words
Unscented Kalman Filter(UKF)/Singular Value Decomposition(SVD)/strong tracking/fading factor/inte-grated navigation分类
航空航天引用本文复制引用
孙磊,黄国勇,李越..改进的强跟踪SVD-UKF算法在组合导航中的应用[J].计算机工程与应用,2017,(10):225-229,240,6.基金项目
国家自然科学基金资助项目(No.51169007) （No.51169007）
云南省科技计划项目(No.2013CA022,No.2012DA005,No.2011DH034) （No.2013CA022,No.2012DA005,No.2011DH034）
云南省中青年学术和技术带头人后备人才培养计划项目(No.2011CI017). （No.2011CI017）
