摘要
目的为解决煤层松软中随钻测量系统测量精度不高的问题。方法提出一种改进的无迹卡尔曼滤波(UKF)和扩展卡尔曼滤波(EKF),分别应用于钻具的姿态滤波算法中并作比较。该方法基于旋转坐标变换的四元数理论和陀螺测量原理,建立钻具姿态传感器数据的非线性观测方程和状态方程,以四元数将测量数据进行转换与更迭,最终消除惯性传感器数据中的误差。与EKF算法相比较,UKF算法利用了UT变换对非线性函数的概率密度分布进行近似,没有忽略高项阶,因此对于非线性分布的统计量有较好的计算精度。结果经仿真验证,UKF的各个滤波误差峰峰值以及标准差小于EKF。结论改进的UKF的滤波算法精度明显高于EKF滤波算法,更加有效地去除惯性传感器中的干扰噪声,有利于提高微机电系统(MEMS)惯性传感器的测量精度,进而提高钻进效率。
Objective In order to solve the problem of the low accuracy of the measurement system.Methods An improved trace Kalman filter(UKF)and extended Kalman filter(EKF)were applied to the posture filtering algorithm for the drill.Based on the quaternionic theory of rotating coordinate transformation and the principle of gyro measurement,this method established the nonlinear observation equation and state equation of the drill attitude sensor data,transformed and changed the measurement data with quaterninion,and finally eliminated the error in the inertial sensor data.The UKF algorithm used the UT transform to approximate the probability density distribution of the nonlinear function without ignoring the high term order,therefor it had a good calculation accuracy for the statistics of the nonlinear distribution compared with the EKF algorithm.Results The simulation showed that the peaks and standard deviation of UKF were less than that of EKF.Conclusion With more accuracy significantly higher than that of the EKF filtering algorithm,the improved UKF filtering algorithm removes more effectively the interference noise in the inertial sensor,which is conducive to improving the measurement accuracy of the inertial sensor of the microelectromechanical system(MEMS)and improving the drilling efficiency.
作者
蔡峰
朱美静
CAI Feng;ZHU Meijing(State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Anhui University of Science and Technology,Huainan Anhui 232001,China;School of Safety Science and Engineering,Anhui University of Science and Technology,Huainan Anhui 232001,China)
出处
《安徽理工大学学报（自然科学版）》
CAS
2024年第1期12-20,共9页
Journal of Anhui University of Science and Technology:Natural Science
基金
安徽省高校协同创新项目(GXXT-2020-057)。
关键词
随钻
姿态解算
MEMS
扩展卡尔曼
无迹卡尔曼
drilling
posture solution
MEMS
Extend Kalman Filter
Unscented Kalman Filter
