考虑运动加速度干扰的无人机姿态估计算法

UAV attitude estimation algorithm considering motion acceleration disturbance

为解决动态环境下无人机导航系统姿态估计易受传感器噪声和运动加速度干扰的难题,提出一种考虑运动加速度干扰的无人机姿态估计算法。首先,建立运动加速度估计模型,根据基于卡尔曼滤波的加速度误差模型和由外部传感器提供的速度信息实现对运动加速度的精确估计,利用运动加速度估计模型获得的运动加速度对加速度计的原始值进行修正,降低动态环境下运动加速度对姿态估计的干扰。随后,搭建基于互补滤波的姿态估计模型,利用磁力计信息和修正后加速度信息构建陀螺仪修正量,对陀螺仪原始值进行修正,设计互补滤波器滤除来自加速度计和磁力计的高频噪声和来自陀螺仪的低频噪声,避免传感器噪声信号对姿态估计的干扰。最后,利用无人机试飞过程中采集的传感器信息对该算法进行实验验证。实验结果表明,该算法可以精确估计无人机机动过程中所产生的运动加速度,有效减弱传感器噪声和运动加速度对姿态估计的干扰,该算法显著提高了无人机导航系统在动态环境下姿态估计的精度和抗干扰能力。

In view of the problem that the attitude estimation of UAV navigation system in dynamic environment is easily interfered by sensor noise and motion acceleration,a new attitude estimation algorithm of UAV considering motion acceleration interference was proposed.First,an acceleration estimation model was established.The acceleration error model based on Kalman filter and the velocity information provided by the external sensor were combined to accurately estimate the motion acceleration.The estimated motion acceleration was used to correct the original value of accelerometer,so as to reduce the interference of motion acceleration in the attitude estimation of navigation system in dynamic environment.Then,an attitude estimation model based on complementary filter was built.The gyroscope correction value was obtained by using magnetometer information and modified acceleration information,and the original gyroscope value was corrected.The complementary filter was designed to filter the high-frequency noise from accelerometer and magnetometer and the low-frequency noise from gyroscope,so as to avoid the interference of sensor noise signal in attitude estimation.Finally,the sensor information collected during flight test was used to simulate and verify the proposed algorithm.Experimental results show that the algorithm could accurately estimate the motion acceleration,reduce the interference of sensor noise and motion acceleration in attitude estimation,and effectively improve the accuracy and anti-interference ability of UAV navigation system in dynamic environment.