采用自适应简化不确定性卡尔曼滤波算法的飞行器导航控制方案

Aero-craft's navigation and control based on adaptive simple uncertain Kalman filter algorithm

针对飞行器绕本体轴高速旋转的飞行过程出现的严重耦合干扰问题和大角加速度和大角速度测量问题,提出了一种自适应简化不确定性卡尔曼滤波算法。该算法使用超球面分布采样点和线性转移等方法简化算法采样计算和采样点的权值计算,提高算法效率;利用模型噪声和线性方程,通过一步预测进行自适应设计,计算滤波值和误差方差矩阵;使用次优噪声估计器推算过程噪声;对过程噪声进行正定判定,防止算法发散。仿真结果表明,这一改进的自适应简化不确定性卡尔曼滤波算法能够有效减少滚转角解算误差和耦合干扰,提高飞行器着陆点的精度。

Aimed at the problems which are serious couple interference and measure high angular velocity and angular acceleration when the aero-craft is circumrotating highly about itself,a new ASUKF (adaptive simple uncertain Kalman filter) algorithm was put forward. The ASUKF algorithm used hyper-spherical distribution and linear displace to accomplish simple sampling calculation and weight value calculation,and improved the algorithm’ s efficiency. Characteristics of noise and linear equation were employed to finish self-adapting control. The ASUKF algorithm used sub-optimal estimation to compute procedural noise,and was judged that it is positive to prevent the algorithm invalidation. The simulation result shows that the ASUKF algorithm can reduce roll angle’s error and couple interference and improve aero-craft’s landing precision.