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

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。

Online Calibration of IMU errors of Inertial Navigation System Based on Innovation-based Adaptive Filtering

Considering the flight environment and motion characteristics of aerospace vehicle, the noise statistical characteristics of the navigation sensors’ errors can’t be completely known, which may seriously degrade the filtering precision or even cause filtering divergence if the conventional Kalman Filter is used. An online calibration method for IMU errors of SINS based on the innovation-based adaptive filtering is proposed, which can overcome the weakness of that the conventional Kalman filter should know the statistical characteristics of the system noise and measurement noise in advance. An error calibration model of 27-D high-order state variables is established, including the installation error, scale factor error and random constant error of IMU, and an online calibration algorithm based on the innovation-based adaptive filtering is proposed, which can adjust the covariance matrices of system noise and measurement noise dynamically. Simulation shows that the proposed method performs higher calibration accuracy and better navigation performance compared to the conventional Kalman filter and Salychev O adaptive filtering algorithm. It is proved by the field test that this method could calibrate the IMU residual errors effectively, improve the navigation accuracy and bring a great convenience for engineering application.