电子测量与仪器学报

章司怡,陈熙源.运动约束辅助的基于 SVD-CKF 的组合导航方法[J].电子测量与仪器学报,2022,36(4):82-89

运动约束辅助的基于 SVD-CKF 的组合导航方法

Motion constraint aided integrated navigation method based on SVD-CKF

DOI：

中文关键词: GNSS / SINS 组合导航系统非线性向心加速度奇异值分解容积卡尔曼滤波

英文关键词:GNSS / SINS integrated navigation system nonlinear centripetal acceleration singular value decomposition cubature Kalman filter

基金项目:国家自然科学基金（61873064）、国家重点研发计划子课题（2017YFC0306303）项目资助

作者	单位
章司怡	1. 东南大学仪器科学与工程学院,2. 微惯性仪表与先进导航技术教育部重点实验室
陈熙源	1. 东南大学仪器科学与工程学院,2. 微惯性仪表与先进导航技术教育部重点实验室

Author	Institution
Zhang Siyi	1. Southeast University Instrument Science and Engineering,2. Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education
Chen Xiyuan	1. Southeast University Instrument Science and Engineering,2. Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education

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中文摘要:

针对强机动车载环境下 GNSS / SINS 组合导航系统非线性增强以及随着迭代次数增加,舍入误差累积,协方差阵不再非负定,导致滤波精度下降甚至滤波发散,提出了运动约束辅助的基于 SVD-CKF 的组合导航方法。在传统运动约束的基础上,引入了向心加速度约束,抑制了载体的前向速度误差发散,避免了载体拐弯时发生侧滑导致的定位精度下降。为了验证算法的有效性进行了车载实验,结果表明相较于经典 SVD-CKF 算法,基于运动约束辅助的 SVD-CKF 算法的经度、纬度误差在 3 个弯道处平均减小了 10. 54%和 44. 64%,东向、北向速度误差平均减小了 50. 87%和 62. 61%。该算法不仅提高了车载环境下的定位精度,也提高了组合导航系统的稳定性和鲁棒性。

英文摘要:

Aiming at the nonlinearity enhancement of the GNSS / SINS integrated navigation system in strong maneuvering vehicular environment and the accumulation of rounding errors as the iteration times increase, the covariance matrix is no longer non-negative, resulting in filtering accuracy decrease even filtering divergence, motion constraint aided integrated navigation method based on SVDCKF was proposed. To restrain the divergence of forward velocity errors and the decrease of position accuracy when sideslip occurs, the centripetal acceleration constraint is introduced based on the traditional motion constraint. In order to verify the effectiveness of the algorithm, a car experiment was carried out. Compared with the standard SVD-CKF, the results show that the longitude and latitude error of the proposed motion constraint aided SVD-CKF algorithm reduced by about 10. 54% and 44. 64% on average at the curve, the east and north velocity errors are reduced by about 50. 87% and 62. 61% on average. This algorithm not only ensures the position accuracy of the carrier in vehicular environment, but also improves the stability and robustness of the integrated navigation system.

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