低成本惯导系统预处理和姿态解算

Low Cost Inertial Navigation System Pretreatment and Attitude Calculation

针对低成本惯性传感器系统中由于系统误差、环境干扰等因素造成的姿态计算数据精度低、易发散等问题,设计了一种加速度计和陀螺仪的误差预处理模型,并使用扩展卡尔曼滤波实现其过程。然后基于扩展卡尔曼滤波算法构建两级噪声方差阵和引入渐消记忆因子的自适应扩展卡尔曼滤波算法,实现姿态角的融合过程。最后采用四元数更新算法求解姿态角。实验结果表明:通过自适应扩展卡尔曼滤波算法使姿态解算精度进一步提高。

In a low-cost inertial sensor system,aiming at the problems including low precision,divergence of attitude data,which were caused by system error and environmental interference,an error preprocessing model based on accelerometer and gyroscope was designed,which completed the process with the assistance of extended Kalman filter.Then,based on the extended Kalman filter algorithm,a two-stage noise variance matrix and an adaptive extended Kalman filter algorithm with fading memory factor were constructed to achieve the fusion process of attitude angle.Finally,the quaternion updating algorithm was used to calculate the attitude angles.Consequently,the experimental results indicate that the adaptive extended Kalman filter algorithm can improve the attitude algorithm accuracy.