基于四元数的MARG传感器姿态测量算法

陀螺仪的漂移、载体的线性加速度和周围局部磁场的干扰是制约MARG传感器姿态测量精度的主要问题,传统的方法利用滤波算法和零速修正技术来减小姿态测量误差。该文基于已有的惯性测量单元,设计了一个基于四元数的扩展Kalman滤波器,通过建立MARG传感器模型,引入传感器偏差补偿和自适应的测量噪声协方差矩阵构造方法来提高姿态测量精度,减小载体线性加速度和周围局部磁场的干扰,实现三自由度的姿态测量。基于惯性测量单元(IMU)的实验结果表明了本文所提算法能显著提高姿态测量精度。

Quaternion-based algorithm for orientation estimation from MARG sensors

Gyroscope bias error,linear system acceleration and magnetic disturbances near the sensor are the major limits affecting the accuracy of orientation estimates based on magnetic,angular rate,and gravit(MARG) sensors.Conventional approaches use filters and zero velocity updates to reduce the orientation estimation error.This paper describes a quaternion-based extended Kalman filter(EKF) that uses the existing inertial measurement unit(IMU).the MARG sensors model uses sensor bias compensation and an adaptive approach to construct the measurement noise covariance matrix to improve the orientation estimation accuracy.The system provides three-dimensional(3-D) orientation measurements and reduces the interference of linear acceleration and magnetic fields near the sensors.Tests based on IMU indicate that the algorithm significantly improves the orientation estimation accuracy.