MEMS四元数卡尔曼滤波算法的电梯姿态估计

Elevator Attitude Estimation Based on MEMS Quaternion Kalman Filter Algorithm

将多维MEMS传感器应用于电梯监测,根据电梯的工作特点,优化四元数互补滤波方法修正陀螺仪数据求解电梯的实时姿态,然后应用卡尔曼滤波方法进一步提高姿态监测精度.实际验证表明,该方法可以提高电梯姿态监测数据的准确性,利用运行的姿态角和加速度峰度进行分析、比对可为电梯安全舒适度评估提供关键的数据依据.

The multi-dimensional MEMS sensor is applied to elevator monitoring.According to the working characteristics of the elevator,the quaternion complementary filtering method is modified to correct the gyroscopedata to solve the real-time attitude of the elevator,and then the Kalman filtering method is applied to further improve the attitude monitoring accuracy.The actual verification shows that the method can improve the accuracy of elevator attitude monitoring data,and use the attitude angle and acceleration kurtosis to analyze and compare,which can provide critical data basis for elevator safety and comfort assessment.