小型无人飞行器机动过程中航姿互补滤波算法研究

磁惯导系统(MINS)广泛应用于小型无人飞行器的导航控制,能对加速度计、磁强计和陀螺仪等传感器的数据进行融合,得到航向与姿态信息,也被称为航姿参考系统(AHRS)。在频域实现数据融合的互补滤波算法是AHRS中的一种可靠姿态估计方法,具有简捷高效的优点。将基于不同传递函数及各种航姿表示形式的姿态互补滤波归纳为统一的广义互补滤波算法(GCF),分析该类算法中的乘性姿态误差,并引入运动加速度补偿方法,可以改善载体机动状态下的姿态精度。数值仿真及实验结果显示,GCF的滤波效果与无人机常用的卡尔曼滤波算法相当,而处理时间仅为后者的1/20,且GCF具有良好的数值稳定性,配合运动加速度补偿算法可有效消除线加速度对航姿测量的不利影响,尤其适合低成本、小型无人机应用场合。

Research on the algorithm of attitude complementary filtering during small UAV maneuver

Magneto-inertial navigation system (MINS) is widely used in navigation control of unmanned aerial vehicle. It can provide attitude and heading information via data fusion of accelerometer, magnetometer and gyroscope. Complementary filter is a concise and efficient solution for data fusion in frequency domain. Therefore, it is also called attitude and heading reference system (AHRS). A generalized complementary filter (GCF) is introduced to consolidate various complementary attitude filters that based on different transfer functions and orientation representations. In this paper, the multiplicative attitude error of this kind of algorithm is analyzed, and compensation algorithm for motional acceleration is also proposed, in order to enhance attitude accuracy under dynamic condition. Numerical simulation and experiment results show that GCF can achieve the same performance of extended Kalman filter, but its execution time is approximately 1 / 20 of the latter''s. Moreover, GCF has ideal numerical stability, and it can eliminate the impacts of linear acceleration with the proposed motion compensation algorithm. Therefore, it is suitable for miniature and low-cost UAV applications.