基于扩展卡尔曼滤波的无人机航姿算法

UAV Attitude and Heading Algorithm Based on Extended Kalman Filter

针对小型无人机在大机动或连续飞行时姿态解算精度低的问题,提出了一种基于扩展卡尔曼滤波(Extended Kalman Filter,EKF)的航姿算法。该算法以机体系加速度分量与陀螺漂移作为待估状态量,建立了非线性滤波模型,在对传感器量测值预处理的基础上完成数据融合,获得了姿态角数据的准确输出,同时,根据外部运动加速度的大小不断调整噪声协方差以达到对EKF的自适应修正,从而抑制无人机航姿解算中磁干扰的影响。经三轴转台实验测试,姿态角的静态误差不超过0.5°,动态误差不超过2°,瞬变磁干扰误差不超过5°,该结果表明该算法能有效提高小型无人机的航姿解算精度。

Aiming at the problem of low attitude resolution accuracy of small unmanned aerial vehicle(UAV)in large maneuver or continuous flight,an attitude algorithm based on extended Kalman filter(EKF)was proposed.In this algorithm,the acceleration component of the aircraft system and gyro drift were taken as the state variables to be estimated,and a nonlinear filtering model was established.Then,the measured values of sensors were preprocessed and data fusion was completed to obtain the accurate output of attitude angle data.At the same time,the noise covariance was constantly adjusted according to the external motion acceleration to achieve the adaptive correction of EKF,and the influence of magnetic interference in UAV attitude calculation was suppressed.Through three-axis turntable experimental test,the static error of attitude angle is not more than 0.5°,the dynamic error is not more than 2°,and the transient magnetic interference error is not more than 5°.It is shown that this algorithm can significantly improve the attitude calculation accuracy of small UAV.