改进UKF算法在无人机姿态解算中的应用

摘要

针对四旋翼无人机姿态解算过程中噪声干扰造成的飞行波动,在无迹卡尔曼滤波(UKF)的基础上进行改进,采用了迭代卡尔曼滤波(ISR-UKF)和结合了衰减因子的单侧最小偏度采样滤波二种滤波方法对噪声进行处理.ISR-UKF在选取了采样点后进行了二次采样,而最小偏度采样滤波将UKF的采样点由2n+1个减少到n+2,分别将以上二种方法在飞机悬停和慢飞的条件下和UKF进行比较,通过仿真结果表明:ISR-UKF在滤波精度上较UKF有所提高,而最小偏度采样在采样点利用率和实时性方面有较好效果.%The noise interference causes the flight fluctuations in the attitude calculation of the quad rotor unmanned aerial vehicle (UAV).In view of this,iterative Kalman filter (ISR-UKF) method and unilateral minimal skew sampling filter method combining with attenuation factor are proposed.The ISR-UKF processes a secondary sampling after selecting the sampling points;the minimal skew filter makes the sampling points in UKF reduces to n+2 from 2n+l.Comparisons to the UKF on hover and slow flight condition demonstrates that the ISR-UKF has higher filtering accuracy and the minimal skew filter is better in sampling efficiency and real-time performance.