摘要
无人机在航姿模式下飞行时,姿态角误差波动较大,根据磁力计、加速度计和陀螺仪的互补性特点,提出一种自适应无迹卡尔曼滤波(AUKF)算法对MEMS传感器数据进行优化求解:以姿态四元数和陀螺漂移为状态量,加速度计和磁力计测量值为观测量,采用梯度下降法优化无迹卡尔曼滤波的关键参数,即过程噪声协方差,以提高四旋翼无人机姿态解算精度。对实际飞行数据的分析表明:分别与常规卡尔曼滤波和传统无迹卡尔曼滤波算法相比,该方法精度最高,可确保小型无人机在各种情况下飞行的稳定性。
When the UAV flies in the attitude modethe attitude angle error fluctuates greatly.According to the complementary characteristics of magnetometeraccelerometer and gyroscopean Adaptive Unscented Kalman Filter(AUKF)algorithm is proposed to optimize the MEMS sensor data.The attitude quaternion and gyro drift are taken as state variablesand the output of accelerator and magnetometer is taken as measurement variables.The gradient descent algorithm is used to optimize the key parameter of Unscented Kalman Filternamelyprocess noise covarianceso as to improve the accuracy of attitude calculation.The analysis of actual flight data shows that the proposed method has the highest accuracy compared with conventional Kalman filter and traditional unscented Kalman filterand can ensure flight stability of small UAVs in various situations.
作者
刘康安
张伟伟
肖永超
叶沐
LIU Kang'an;ZHANG Weiwei;XIAO Yongchao;YE Mu(Shanghai University of Engineering Science,Shanghai 201000,China;Tsinghua University,Beijing 100000,China)
出处
《电光与控制》
CSCD
北大核心
2022年第7期126-131,共6页
Electronics Optics & Control
关键词
无人机
无迹卡尔曼滤波
姿态估计
数据融合
UAV
Unscented Kalman Filter(UKF)
attitude estimation
data fusion
