基于新息自适应滤波的惯性测量单元误差在线标定方法研究

发布时间：2018-06-17 18:26

本文选题：控制科学与技术 + 惯性导航系统　；参考：《兵工学报》2016年07期

【摘要】：考虑到空天飞行器飞行环境和运动特性下导航传感器误差的噪声统计特性不可能完全精确已知,若使用常规卡尔曼滤波进行在线标定,将会导致滤波精度下降甚至发散。设计一种基于新息自适应滤波方法的惯性测量单元(IMU)误差在线标定方案和算法,克服常规卡尔曼滤波需预先知道噪声统计特性的不足,设计包含IMU安装误差、刻度因子误差和随机常值误差在内的27维高阶状态变量的误差标定模型,分析提出可同时对系统噪声和量测噪声协方差矩阵进行动态调整的新息自适应滤波在线标定算法。仿真验证实验表明,相较于采用常规卡尔曼滤波以及Salychev O自适应滤波算法进行在线标定,所设计的新息自适应滤波在线标定方法能更有效实现对IMU误差的动态标定及补偿,进一步提高了惯性导航系统精度。实物验证实验表明,该方法可有效标定IMU误差残差,提高导航精度,为工程应用带来较大便利。
[Abstract]:In view of the fact that the noise statistics of navigation sensor errors under the flight environment and motion characteristics of aircrafts can not be fully known, if the conventional Kalman filter is used for on-line calibration, the filtering accuracy will decrease or even diverge. An on line calibration scheme and algorithm for inertial measurement unit (IMU) errors based on innovative adaptive filtering method is designed. It overcomes the deficiency of conventional Kalman filter which needs to know the statistical characteristics of noise in advance. The design includes IMU installation error. The error calibration model of 27 dimensional high order state variables including calibration factor error and random constant error is analyzed and a novel adaptive filtering online calibration algorithm is proposed which can dynamically adjust the covariance matrix of system noise and measurement noise simultaneously. The simulation results show that compared with the conventional Kalman filter and Salychev O adaptive filtering algorithm, the proposed method can effectively calibrate and compensate the IMU error. The precision of inertial navigation system is further improved. The experimental results show that this method can effectively calibrate the error residuals of IMU, improve the navigation accuracy, and bring great convenience for engineering application.
【作者单位】：南京航空航天大学导航研究中心;
【基金】：国家自然科学基金项目(61533008;61533009;61374115) 国家留学基金委资助项目(2012年) 江苏省六大人才高峰资助项目(2013-JY-013) 江苏省高校优势学科建设工程项目(2014年) 中央高校基本科研业务费专项资金项目(NP2015406;NJ20150012;NP20152212;NS2014031) 南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20150315)
【分类号】：V249.3

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