Data fusion for a GPS/INS tightly coupled positioning system with equality and inequality constraints using an aggregate constraint unscented Kalman filter
Data fusion for a GPS/INS tightly coupled positioning system with equality and inequality constraints using an aggregate constraint unscented Kalman filter
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DOI：
10.1080/14498596.2018.1544937
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发表时间：
2018-11
影响因子：
1.9
通讯作者：
Hang Yu;Zengke Li;Jian Wang;Houzeng Han
中科院分区：
文献类型：
--
作者：
Hang Yu;Zengke Li;Jian Wang;Houzeng Han
摘要众所周知，无迹卡尔曼滤波（UKF）已成功地实现了惯性和全球定位系统（GPS）测量的集成。提出了一种将聚合约束法与UKF相结合的方法来解决等式和不等式约束下的GPS/INS组合问题。该算法综合了聚合约束方法和UKF的特点，并以计算效率高的方式实现结果。数值算例表明，该算法避免了大量的计算开销，并能达到与现有的约束卡尔曼滤波器相同水平的良好的适用性。
ABSTRACT It is well known that the unscented Kalman filter (UKF) has been successfully implemented for the integration of inertial and global positioning system (GPS) measurements. This paper proposes combining an aggregate constraint method with the UKF to solve the GPS/inertial navigation system (INS) integration with equality and inequality constraints. The proposed algorithm comprehensively combines the characteristics of the aggregate constraint method and the UKF, and achieves the results in a computationally efficient way. A numerical example shows the proposed algorithm avoids large computational expense and can achieve the same level of good applicability compared with the existing constrained Kalman filter.