基于自适应无迹卡尔曼滤波的四旋翼无人机姿态解算  

Attitude Calculation of Quadrotor UAV Based on Adaptive Unscented Kalman Filter

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)

机构地区：[1]上海工程技术大学,上海201000 [2]清华大学,北京100000

出　　处：《电光与控制》2022年第7期126-131,共6页Electronics Optics & Control

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 modethe attitude angle error fluctuates greatly.According to the complementary characteristics of magnetometeraccelerometer and gyroscopean Adaptive Unscented Kalman Filter(AUKF)algorithm is proposed to optimize the MEMS sensor data.The attitude quaternion and gyro drift are taken as state variablesand 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 Filternamelyprocess noise covarianceso 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 filterand can ensure flight stability of small UAVs in various situations.

关 键 词：无人机 无迹卡尔曼滤波 姿态估计 数据融合 

分 类 号：V249[航空宇航科学与技术—飞行器设计] TP391[自动化与计算机技术—计算机应用技术]