摘要
EKF是SINS/LBL紧组合导航系统中常用的数据融合方式,但其泰勒线性化会带来截断误差,从而影响系统定位精度。本文主要研究了SINS/LBL紧组合系统量测方程的非线性对系统位置校正性能的影响,建立了SINS/LBL紧组合系统的EKF状态方程和量测方程,并详细推导了UKF滤波的非线性量测方程,为进一步简化计算量,使用卡尔曼滤波的一步状态预测方程等价代替UKF状态预测UT变换。最后的仿真结果表明UKF能明显改善位置项的定位精度,定位精度提高约15%~20%,所提出的UKF算法在SINS/LBL紧组合导航系统中应用效果较好,具有很强的实用性。
An extended Kalman filter（EKF） is commonly used in data fusion method for SINS/LBL tightly coupled navigation system, but its Taylor linearization could bring truncation errors, which affects the system positioning accuracy. This paper studies the measurement equation＇s nonlinearity effect of the SINS/LBL tightly coupled system on the position correction performance of the system, establishes EKF state equation and measurement equation of the SINS/LBL tightly coupled system, and derives the unscented Kalman filter（UKF） non-linear measurement equation. In order to further simplify the computation, a step state prediction equation of Kalman filtering is used to equivalently replace the UT transformation of UKF prediction. Simulation results show that the UKF can significantly improve the position location accuracy, the positioning accuracy is improved by about 15% to 20%, and the proposed UKF algorithm has a good application effect in the SINS/LBL tightly coupled navigation system with strong practicability.
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2016年第5期638-642,共5页
Journal of Chinese Inertial Technology
基金
国家自然科学基金资助项目(51375088)
微惯性仪表与先进导航技术教育部重点实验室基金(201403)
优秀青年教师教学科研资助计划(2242015R30031)
基于大数据架构的公安信息化应用公安部重点实验室(浙江警察学院)开放课题资助项目(2015DSJSYS002)
关键词
AUV
捷联惯导
长基线
UKF
autonomous underwater vehicle
SINS
long base line
unscented Kalman filter
