基于模糊预测与扩展卡尔曼滤波的野值剔除方法

Outlier Removal Algorithm Based on Fuzzy Prediction and Extended Kalman Filter

针对基于微机电系统无人机的野值的辨识和处理,设计了一种基于模糊预测与扩展卡尔曼滤波(EKF)的野值剔除方法.首先,将角速度模长引入梯度下降法中,通过动态调整梯度步长,提高模糊预测量的精度,有效避免模糊预测时对阈值依赖性较高及野值剔除后造成的数据点丢失;其次,以新息作为野值的判别准则,利用加速度计测量值信任度因子调整新息判别阈值,从而剔除观测传感器的野值;然后,通过共轭梯度法将模糊预测值转化为姿态四元素重新修正卡尔曼滤波的状态估计.最后,基于所搭建的共轴双桨无人机实验平台系统验证所提算法的有效性.实验结果表明:在共轴双桨无人机悬停、强机动情况下,所提算法能保证共轴双桨无人机的稳定飞行,有效提高共轴双桨无人机的姿态跟踪精度和稳定性.

Aiming to identify and process outliers based on micro-electro-mechanical systems unmanned aerial vehicle(UAV),we design an outlier removal algorithm based on fuzzy prediction and extended Kalman filter(EKF).First,we introduce the length of angular velocity into the gradient descent method and improve the accuracy of the fuzzy prediction by dynamically adjusting the gradient step.This effectively avoids the high dependence on the threshold value in the fuzzy prediction and the loss of data points after the outliers are removed.Second,we consider innovation as the criterion for the outlier and adjust the innovation threshold by using the accelerometer measurement trust factor to eliminate the outliers of the observation sensor.Third,we use the conjugate gradient method to convert the fuzzy prediction value into quaternion and recorrect the state estimation of the EKF.Finally,based on the constructed coaxial twin-propeller UAV experimental platform system,we verify the effectiveness of the proposed algorithm.The experimental results show that the proposed algorithm can ensure the stable flight of the coaxial twin-propeller UAV under hovering and strong maneuver and effectively improve the attitude tracking accuracy and stability.