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
段镖 1徐尤松 2张勇3
作者信息
- 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
- 折叠
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.
Abstract
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Key words
air data estimation/satellite positioning system/inertial navigation system(INS)/flush air data sensing system(FADS)/cubature Kalman filtering(CKF)引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据