摘要
为提高捷联惯导系统级标定算法的滤波收敛速度,提出一种Sage-Husa自适应滤波快速系统级标定方法。分析Kalman滤波的噪声参数,对量测噪声方差阵进行自适应辨识。采用六位置静态标定方案,在满足滤波可观测性的前提下,设计中间参数对滤波模型进行降维处理,降维后的模型能够更好地适用于Sage-Husa自适应滤波。实验结果表明:与常规Kalman滤波系统级标定方法相比,2种滤波方法均能标定出所有IMU误差参数,且标定的相对误差均小于0.6%,但标定时间由5 min缩短至6 s,提高了捷联惯导系统级标定的速度。
In order to improve the filter convergence speed of the systematic calibration algorithm of strapdown inertial navigation system(SINS),this paper proposes a fast systematic calibration method for Sage-Husa adaptive filter.The noise parameters of the Kalman filter are analyzed to carry out the adaptive identification of the measurement noise variance matrix.A six-position static calibration scheme is used to reduce the dimension of the filter model by designing intermediate parameters on the premise of satisfying observability of the filter.The simplified model after dimension reduction can be better applied to Sage-Husa adaptive filter.The experimental results show that,compared with the conventional Kalman filter systematic calibration method,both filter methods can calibrate all inertial measurement unit(IMU)error parameters.The relative errors of calibration are all less than 0.6%.However,the calibration time shortens from 5 min to 6 s,which improves the speed of SINS systematic calibration.
作者
赵桂玲
谭茂林
姜子昊
ZHAO Guiling;TAN Maolin;JIANG Zihao(School of Geomatics and Geographic Sciences,Liaoning Technical University,Fuxin 123000,China)
出处
《兵器装备工程学报》
CAS
CSCD
北大核心
2023年第3期226-231,共6页
Journal of Ordnance Equipment Engineering
基金
辽宁省自然科学基金项目(2020-MS-303
2020-BS-259)
辽宁省教育厅一般项目(LJ2020JCL015)。
