基于UKF的FADS/INS融合大气数据估计

针对飞行器在高速飞行时受气流干扰、惯性数据易发散等问题，从传感器数据融合角度出发，提出了通过无迹卡尔曼滤波(UKF)融合嵌入式大气数据观测系统(FADS)和惯性导航系统(INS)估计飞行器实时大气数据的算法。算法使用高维度非线性方程对惯性系统和大气系统间的关系建模，结合FADS与INS的数据，计算飞行器速度和高度，进而估算出攻角、侧滑角等参数。实验结果显示，与INS直接解算、扩展卡尔曼滤波(EKF)融合等原有估计方法相比，文章所述的算法在估计精度和系统稳定性方面均有所提高。

Air Data Estimation of FADS/INS Fusion Based on UKF

To solve the problems of airflow interference and inertial measurement data dispersion when the aircraft is flying at high speed，an algorithm with sensors data fusion is proposed，which estimate the real-time air data of the aircraft by Flush Air Data Sensor System(FADS）and Inertial Navigation System(INS）based on Unscented Kalman Filtering(UKF).The algorithm uses high-dimensional nonlinear system to model the relationship between inertial system and air system.Combined with the data of FADS and INS，the air speed is calculated，and then air data such as angle of attack and angle of sideslip are estimated.In the experimental results，compared with the original estimation methods such as INS direct solution and extended Kalman filter(EKF）fusion，the accuracy and system stability of estimation in this paper are improved.