无人机自适应姿态角度估计仿真

UAV Adaptive Attitude Angle Estimation Simulation

针对复杂环境下传感器噪声未知且不断变化,会导致姿态融合结果不准确的问题,设计了一种基于单新息自适应算法的卡尔曼滤波器,对加速度计和陀螺仪噪声协方差进行在线估计。首先,介绍了能够结合各个传感器优势的无人机姿态融合算法。然后,设计了采用基于单新息自适应算法的卡尔曼滤波器,给出了能够在线估计加速度噪声协方差R和陀螺仪噪声协方差Q的自适应算法。MATLAB仿真表明单新息自适应卡尔曼滤波器在环境噪声变化时,能够更准确地获得无人机的姿态信息,提高了姿态融合精确度,提高了滤波器的鲁棒性。

Because the sensor noise is unknown and constantly changes in complex environment,the pose fusion result will be inaccurate.This paper uses the single innovation based adaptive algorithm to online estimate the acceleration noise covariance and gyroscope noise covariance,aims to improve the attitude estimation accuracy of Unmanned Aerial Vehicle(UAV).Firstly,the UAV attitude fusion algorithm which can combine the advantages of various sensors is introduced.Then,a Kalman filter based on single innovation adaptive algorithm is designed,and an adaptive algorithm that can estimate the acceleration noise covariance and the gyro noise covariance Q is given.The MATLAB simulation shows that the single-innovation adaptive Kalman filter can obtain the attitude information of the drone more accurately when the ambient noise changes,and improve the attitude fusion accuracy.