基于双回路扩展卡尔曼滤波的惯性平台连续自标定

Continuous Self-Calibration of Inertial Platform Based on Dual Extended Kalman Filter

为解决传统的惯性平台连续自标定中,由于系统非线性强、状态向量维数大引起的滤波收敛速度慢、对滤波初始条件敏感等问题,研究了一种双回路扩展卡尔曼滤波方法。首先给出了平台连续自标定的误差模型;然后根据加速度计误差与导航误差之间的关系,对加速度计输出进行预滤波得到加速度计输出误差;同时通过分析陀螺仪误差在平台连续自标定过程中的传播特性,将耦合在加速度计输出误差中的陀螺仪误差解耦;最后以陀螺仪误差和加速度计输出为观测量,建立了陀螺仪和加速度计的扩展卡尔曼滤波方程,分别对陀螺仪和加速度计误差系数进行标定,实现双回路扩展卡尔曼滤波。仿真结果表明,该方法能够在900 s内以低于0.05%的相对误差标定出所有的平台误差系数,并且对滤波初始条件不敏感,可以有效地扩展连续自标定方法的实际应用。

In view of the problems of slow convergence and sensitivity to initial conditions in the traditional continuous self-calibration of an inertial platform because of the nonlinearity of the system and the large number of dimensions of the state vector, the dual extended Kalman filter is researched in this paper. Firstly, the error models of the continuous self- calibration are given. Then the output error of the accelerometer is pre-fihered according to the relationship between the aecelerometer error and the navigation velocity error. At the same time, the gyro error is deconpled from the output error of the accelerometer by analyzing its propagation characteristics in the continuous self-calibration. At last, the dual extended Kalman filter is carried out by using the gyro error and the accelerometer output as the observations respectively to establish the filter equations of the gyro and the accelerometer, which can reduce the nonlinearity of the system and the number of the dimensions of the state vector. Simulation results indicate that the continuous self-calibration method based on the dual extended Kalman filter can calibrate all the error coefficients of the inertial platform with relative error lower than 0.05% within 900s and is not sensitive to the initial conditions, which is beneficial to the application of the continuous self- calibration of the inertial platform.