基于四元数衍生无迹卡尔曼滤波的二段式多旋翼无人机姿态估计算法

对传统多旋翼无人机姿态估计算法难以兼顾高精度、强实时性以及抗干扰能力差的问题,首先基于一种计算量较小的衍生无迹卡尔曼滤波算法,在量测更新中,将加速度数据和磁力计数据分为两个阶段进行姿态四元数校正处理,然后从旋转四元数的本质出发,推测出四元数各元素分别包含着不同的姿态角信息,最后将校正四元数分别乘上为降低校正过程中的相互干扰所设计的系数,提出一种基于四元数衍生无迹卡尔曼滤波的二段式多旋翼无人机姿态估计算法.通过使用PIXHAWK飞控数据,与传统姿态估计算法进行仿真实验对比,实验表明,本文提出算法与传统使用扩展卡尔曼滤波(EKF)或无迹卡尔曼滤波(UKF)的姿态估计算法相比,在实时性、解算精度和抗干扰能力方面有较大提升.

Quaternion derivative unscented Kalman filter-based two-step attitude estimation algorithm for multi-rotor unmanned aerial vehicle

The traditional attitude estimation algorithm for multi-rotor UAV is difficult to balance high-precision, strong real-time and has poor anti-interference ability. To address this problem, a derivative Unscented Kalman filter algorithm (DUKF) with a relatively small computational complexity is used firstly. In the measurement update, the acceleration data and magnetometer data are divided into two phases for attitude quaternion correction processing. Secondly, according to the nature of quaternion, the assumption that each element of the quaternion contains different attitude angle information is made. Finally, the calibration quaternion is multiplied by the coefficient designed to reduce the mutual interference in the calibration process. A quaternion DUKF-based two-step attitude estimation algorithm for multi-rotor UAV is proposed. By using the PIXHAWK flight control data, the simulation results are compared with traditional attitude estimation algorithms. Experiments show that compared with traditional attitude estimation algorithms using EKF or UKF, the algorithm proposed has great improvement in real-time performance, resolution accuracy and anti-interference ability.