跨音速大气/惯性攻角两步融合算法

攻角是飞机飞行的重要状态参数,也是飞控及导航系统必需的参数,现代战机对攻角精度要求越来越高.大气数据系统在跨音速条件下性能严重下降,大气攻角精度受到很大影响.针对试验机大气数据传感器的配置特点,该文设计了一种基于变参数互补滤波器与神经网络的跨音速大气/惯性攻角两步融合算法,实现惯导系统和大气系统的攻角信息融合.首先进行跨音速惯导天向回路解算,而后利用互补滤波器与神经网络对大气/惯性攻角进行互补融合与修正,使最终的融合修正攻角平稳、可靠,逼近真实攻角.本文利用某型飞机的实际试飞数据对两步融合算法进行了验证.结果表明,融合修正后的攻角能够基本去除跨音速飞行阶段原始大气攻角的剧烈波动,并与真实攻角吻合.

Two-Step Fusion Algorithm for ADS/INS Angles of Attack on Transonic Flight

The angle of attack (AOA) is an important state parameter of flight. It is also a necessary parameter in flight control and navigation systems. The modem fighters have increasing demands on the AOA accuracy. The performance of air data systems (ADS) seriously declines under transonic conditions. The accuracy of ADS-AOA is seriously affected. In view of air data sensor configuration features of testing plane, a transonic ADS-INS AOA two-step fusion algorithm based on variable parameters complementary filter and neural network is designed in this paper to achieve fusion of INS and ADS. The divergent transonic INS vertical velocity is resolved. Variable parameters complementary filter and neural network are used to fuse and correct ADS-INS AOA. The result approaches the true AOA with stability and reliability. Actual flight data are used to validate the two-step fusion algorithm. The results show that the AOA after fusion and correction can eliminate violent fluctuations of ADS-AOA under transonic condition. Its tendency coincides with the true AOA with small error .