基于Mahony滤波和EKF融合的姿态解算方法  

Attitude solution method based on Mahony filtering and EKF fusion

作　　者：朱鹏 陈威平 石颖 何林彬 谢文武 余超 ZHU Peng;CHEN Weiping;SHI Ying;HE Linbin;XIE Wenwu;YU Chao(College of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China)

机构地区：[1]湖南理工学院信息科学与工程学院,湖南岳阳414006

出　　处：《传感器与微系统》2023年第12期160-163,168,共5页Transducer and Microsystem Technologies

基　　金：湖南省教育厅优秀青年项目(20B267,20B269);湖南省自然科学基金资助项目(2020JJ4341)。

摘　　要：随着微机电系统(MEMS)的发展,基于惯性测量单元(IMU)的捷联式惯性导航系统(SINS)成为了行人导航的理想选择。针对SINS定位精度低、器件噪声大的问题,提出一种改进的Mahony滤波器和扩展卡尔曼滤波(EKF)相结合的姿态解算算法。首先,该算法基于SINS误差量建立模型;再以Mahony滤波加速度与捷联惯导姿态解算加速度之差作为系统的观测量;最后,应用EKF融合陀螺仪、加速度计的测量数据得到定位结果。实验结果表明,在不同的运动状态下,与EKF和Mahony滤波器相比,平均定位误差分别降低了73.27%和65.76%以上,该融合算法能有效估计行人姿态,减少航向漂移,提高定位精度。With the development of MEMS,strapdown inertial navigation system(SINS)based on inertial measurement unit(IMU)has become the ideal choice for pedestrian navigation.Aiming at the shortcomings of SINS,such as low positioning precision and high device noise,an improved attitude solution algorithm combining Mahony filtering and extended Kalman filtering(EKF)is proposed.In this algorithm,firstly,an equation of model is established based on the errors of the SINS.Next,the difference between the Mahony filtered acceleration and the calculated acceleration of SINS is the observed quantity of the system.Last,the measured data of gyroscope and accelerometer are fused by EKF to obtain the positioning.The experimental results show that compared with EKF and Mahony filter,the average positioning error is reduced by more than 73.27%and 65.76%,respectively,in different motion states.The fusion algorithm can effectively estimate pedestrian attitude and reduce heading drift,and improve positioning precision.

关 键 词：Mahony滤波 姿态估计 扩展卡尔曼滤波 数据融合 惯性导航 

分 类 号：U666.1[交通运输工程—船舶及航道工程] TP212[交通运输工程—船舶与海洋工程]