旋转导向钻具姿态的无迹卡尔曼滤波方法

三轴磁通门数据中包含的磁干扰和三轴加速度计数据中包含的大量低频振动加速度，严重影响了旋转导向钻具姿态测量的精度。为去除传感器数据中的干扰噪声，快速解算出准确的钻具姿态，提出了一种改进的无迹卡尔曼（UKF）迭代滤波算法。此方法基于旋转坐标变换的四元数理论，建立钻具姿态传感器数据的非线性观测方程，利用陀螺测量原理构造状态方程，将滤波后观测数据以四元数的形式更新状态方程三轴角速度，从而实现UKF迭代滤波，最终消除传感器数据中的干扰噪声。实验室及胜利油田河31井区斜160井的实测数据滤波结果表明，此方法可有效滤除姿态传感器中的干扰噪声，提高旋转导向钻具姿态测量的准确性。

An unscented Kalman filter method for attitude measurement of rotary steerable drilling assembly

The three-axial fluxgate data contains magnetic interference and there exists a great deal of low-frequency vibration acceleration in the triaxial accelerometer data, these noises will heavily affect the accuracy of the attitude measurement of the rotary steerable drilling assembly. To filter noises in the measured data and swiftly get an accurate attitude of the drilling assembly, an improved Unscented Kalman Filter(UKF)algorithm based on the quaternion theory of rotating coordinate transformation was proposed, consequently, a nonlinear observation equation for attitude data of the drilling assembly was established. Moreover, a state equation was constructed based on the principle of gyroscope. Observation data after filtering were used for updating three-axis angular velocities of the drilling assembly in the way of quaternion, thus the UKF iterative filtering was implemented, finally noises in the sensor data were eliminated. The method was applied to filtering the measured data derived both from the laboratory and from the HE31-XIE160 well of the Shengli oilfield, the filtering results showed that this method can filter the noise of sensors effectively and enhance the accuracy of the attitude measurement of the rotary steerable assembly.