基于平方根容积卡尔曼滤波的四旋翼无人机的姿态解算

Attitude Calculation of Quadrotor Unmanned Aerial Vehicle Based on Square-root Cubature Kalman Filter

为了提高四旋翼无人机姿态解算的精度,提出了基于平方根容积卡尔曼滤波(square-root cubature Kalman filter,SCKF)的多传感器数据融合策略。基于加速度计、磁力计和陀螺仪输出的数据,采用了四元数的姿态解算方法,避免了单一传感器获得的姿态角误差过大的问题,解决了扩展卡尔曼滤波(extend Kalman filter,EKF)精度低以及无迹卡尔曼滤波(unscented Kalman filter,UKF)、容积卡尔曼滤波(cubature Kalman filter,CKF)协方差矩阵正定性丧失的问题。设计了基于pixhawk飞控板的实验方案。通过实验数据表明,与传统的EKF、UKF、CKF算法相比,SCKF的精度最高。且与UKF、CKF算法相比,SCKF具有计算时间少、数值计算稳定性强等优势。

In order to improve the accuracy of attitude calculation of quadrotor unmanned aerial vehicle(UAV),a multi-sensor data fusion strategy based on square-root cubature Kalman filter(SCKF) is proposed. According to the data outputted from accelerometer,magnetometer and gyroscope,the attitude solution method of quaternion is adopted,the problem that the error of attitude angle obtained by a single sensor is too large is avoided.Also,the problem of low accuracy of extend Kalman filter(EKF) and positive qualitative loss of unscented Kalman filter(UKF),cubature Kalman filter(CKF) covariance matrix are solved. An experimental scheme based on pixhawk flight controller is designed. The experimental data shows that,comparing with the traditional EKF,UKF and CKF algorithm,the SCKF has the highest accuracy,and comparing with UKF,CKF algorithm,SCKF has the advantages of less computation time and better stability of numerical calculation.