基于MEMS陀螺仪和加速度计的自适应姿态测量算法

An Adaptive Attitude Measurement Algorithm Based on MEMS Gyroscope and Accelerometer

针对MEMS陀螺仪以及加速度计传感器单独测姿时,传感器数据中存在复杂噪声和测量误差导致测量得不到最优姿态角的问题,设计了一种基于MEMS陀螺仪和加速度计的自适应姿态测量算法.算法采用扩展卡尔曼滤波方法实现数据融合,并且在利用Allan方差估计MEMS陀螺动态噪声的同时,加入了遗忘因子和限定记忆的算法思想,从而实时地跟踪数据的量测噪声,实时修正角度估计误差,有效地提高了姿态测量系统的精度.实验结果表明,二者组合定姿可实现高精度的姿态测量,验证了算法良好的动态噪声抑制能力,提高了系统对环境变化的适应性.

Aiming at the problem that the MEMS gyroscope and the accelerometer sensor measure separately,the measurement data can not get the optimal Attitude angle due to the complicated noise and the measurement error.An adaptive attitude measurement algorithm is designed which is based on the MEMS gyroscope and the accelerometer sensor.The algorithm uses extended Kalman filter to achieve data fusion.In addition,Allan variance is used to estimate the dynamic noise of the MEMS gyroscope.At the same time,the forgetting factor and memory-limited algorithm are added to track the measured noise of the data in real time,and the angle estimation error is corrected in real time,which effectively improves the accuracy of attitude measurement system.The experimental results show that the combination of the MEMS gyroscope and the accelerometer can achieve high precision attitude measurement,and verify the good dynamic noise suppression ability of the algorithm and improve the adaptability of the system to environmental changes.