多源信息融合的飞行器大气数据估计算法  

Estimation Algorithm for Aircraft Atmospheric Data Integration Using Multi⁃source Information Fusion

作　　者：段镖[1] 徐尤松 张勇 DUAN Biao;XU Yousong;ZHANG Yong(China Helicopter Design and Research Institute,Jingdezhen 333001,China;College of Aeronautics,Nanjing University of Aeronautics&Astronautics,Nanjing 210016,China;UAV Research Institute,Nanjing University of Aeronautics&Astronautics,Nanjing 210016,China)

机构地区：[1]中国直升机设计研究所,景德镇333001 [2]南京航空航天大学航空学院,南京210016 [3]南京航空航天大学无人机研究院,南京210016

出　　处：《南京航空航天大学学报》2024年第4期750-755,共6页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(52272369)。

摘　　要：针对飞行器在高速飞行时受气流干扰、惯性高度易发散等问题,从传感器数据融合角度出发,提出了容积卡尔曼滤波(Cubature Kalman filter,CKF)融合嵌入式大气数据观测系统和惯性导航系统(Inertial navigation system,INS)估计飞行器实时大气数据的算法。算法使用非线性方程对惯性系统、卫星定位系统和大气系统间的关系建模,结合传感器的数据,计算飞行器速度和高度,进而估算出迎角和侧滑角等参数。实验结果显示:本文所提出的方法在估计气流角和马赫数方面具有较高的精度和较强的稳定性。To address airflow interference and inertial altitude dispersion during high-speed aircraft flight,a novel algorithm integrating cubature Kalman filter(CKF),air data observation system,and inertial navigation system(INS)is proposed for real-time aircraft air data estimation through sensor data fusion.The algorithm utilizes nonlinear equations and models interactions among the inertial system,satellite positioning system,and air data system to integrate sensor data for computing aircraft velocity and altitude,followed by estimation of parameters such as angle of attack and sideslip.Experimental results demonstrate the high accuracy and stability of the proposed method in estimating airflow angle and Mach number.

关 键 词：大气数据估计 卫星定位系统 惯性导航系统 嵌入式大气数据传感系统 容积卡尔曼滤波 

分 类 号：V241[航空宇航科学与技术—飞行器设计]