作者：刘震, 王雪梅, 倪文波

作者单位：西南交通大学机械工程学院, 四川 成都 610031

关键词：姿态解算;运动加速度;互补滤波;卡尔曼滤波;MEMS传感器

摘要：

针对传统姿态参考系统姿态解算容易受到载体运动加速度的干扰，导致系统精度变低、稳定性变差等问题，提出一种改进的卡尔曼滤波算法。该算法建立基于四元数的惯性系统姿态解算数学模型，并根据载体运动加速度的大小，适时调整卡尔曼滤波器的量测噪声方差的大小，以此减弱卡尔曼滤波过程中运动加速度对姿态角解算精度的影响。采用MEMS三轴陀螺仪、加速度计和磁阻传感器完成载体在电梯升降过程中的测量，对实验测量数据进行姿态解算，结果表明改进后的卡尔曼滤波算法能够有效减小运动加速度对姿态解算的影响，姿态角的均方根误差相对于传统的姿态参考系统降低约40%。

Research on attitude angle measurement with high precision based on MEMS sensors

LIU Zhen, WANG Xuemei, NI Wenbo

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Abstract: Since the conventional attitude and heading reference system are vulnerable to the interference of motion acceleration, an improved Kalman filter algorithm was proposed in the paper. Quaternion attitude mathematical model based on inertial system was built up. The improved Kalman filter algorithm can reduce the impact of motion acceleration on attitude algorithm in the process of Kalman filter by timely adjusting the observation noise variance according to the motion acceleration of vehicle. The experimental data were measured by MEMS three-axis gyroscope, accelerometer and magnetoresistive sensors during riding up and down in the elevator. The results demonstrate that the improved Kalman filter algorithm can significantly reduce the impact of motion acceleration on attitude estimation. And compared to traditional attitude and heading reference system, attitude RMSE with improved Kalman filter algorithm has decreased by 40%.

Keywords: attitude estimation;motion acceleration;complementary filtering;Kalman filter;MEMS sensor

2017, 43(2): 6-12  收稿日期: 2016-06-12;收到修改稿日期: 2016-07-22

基金项目: 

作者简介: 刘震(1990-),男,陕西西安市人,硕士研究生,专业方向为测控技术及应用。

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