Li Jing, Su Lichen, Wang Fang, Li Kailong, Zhang Lili
Information Engineering College, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China.
Sensors (Basel). 2022 Jul 4;22(13):5036. doi: 10.3390/s22135036.
In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static condition, it cannot suppress the scale factor errors thoroughly during the maneuvering process of the vehicle due to the two degrees of rotation freedom. The self-calibration method has been studied by many researchers. However, traditional calibration methods need several hours to converge, which is unable to meet the demand for quick response to positioning and orientation. To solve the above problems, we do the following work in this study: (1) we propose a 39-dimensional online calibration Kalman filtering (KF) model to estimate all calibration parameters; (2) Error relationship between calibration parameters error and navigation error are derived; (3) A backtracking filtering scheme is proposed to shorten the calibration process. Experimental results indicate that the proposed method can shorten the calibration process and improve the calibration accuracy simultaneously.
在高精度双轴旋转惯性导航系统（RINS）领域，陀螺仪和加速度计的校准精度至关重要。虽然旋转调制可以在静态条件下抑制由比例因子误差和偏置误差引起的导航误差，但由于存在两个旋转自由度，在车辆的机动过程中它无法彻底抑制比例因子误差。许多研究人员对自校准方法进行了研究。然而，传统的校准方法需要几个小时才能收敛，无法满足对定位和定向快速响应的需求。为了解决上述问题，我们在本研究中开展了以下工作：（1）提出一个39维的在线校准卡尔曼滤波（KF）模型来估计所有校准参数；（2）推导校准参数误差与导航误差之间的误差关系；（3）提出一种回溯滤波方案以缩短校准过程。实验结果表明，所提方法能够同时缩短校准过程并提高校准精度。
