摘要
针对捷联惯性测量单元(IMU)噪声大、精度低的缺点和常规的姿态解算算法精度不高等问题,提出了一种互补滤波和卡尔曼滤波相结合的融合算法。该算法基于姿态角微分方程建立系统的状态方程模型,利用互补滤波后的姿态角作为系统的观测量,再应用扩展卡尔曼滤波(EKF)算法融合了陀螺仪、加速度计和电子罗盘的测量数据。为验证该算法有效性,用带有传感器的开发板依次进行静态和动态测试,实验结果表明:结合了互补滤波和卡尔曼滤波的融合算法,在静态时能够抑制姿态角漂移和滤出噪声,在动态时能够快速跟踪姿态的变化,提高了姿态角的解算精度。
Aiming at problem of high noise,low precision of inertial measurement unit( IMU) and low precision of classical attitude solution algorithm,a fused algorithm with complementary filtering and Kalman filtering is proposed. The algorithm establish the state equation model based on differential equation of attitude angle and choose attitude angle after compensation filtering as the observation of system. Use EKF algorithm fused measured data of gyro,accelerometer and electronic compass. To verify that the algorithm is effective,use development board with inertial sensors to test in static and dynamic condition. The results of experiments show that the algorithm fused with complementary filtering and Kalman filtering can constraint drift and noise of attitude angle in static condition and track change of attitude angle quickly in dynamic condition,thus precision of attitude angle estimation is improved.
作者
张栋
焦嵩鸣
刘延泉
ZHANG Dong JIAO Song-ming LIU Yan-quan(Department of Automation, North China Electric Power University, Baoding 071003, China)
出处
《传感器与微系统》
CSCD
2017年第3期62-65,69,共5页
Transducer and Microsystem Technologies
关键词
卡尔曼滤波
互补滤波
姿态估计
数据融合
惯性测量单元
Kalman filtering
complementary filtering
attitude estimation
data fusion
inertial measurement unit(IMU)