摘要
对飞行器大气数据进行估计是获取飞行状态的重要一环,是实现飞行器控制和稳定飞行的基础。通过研究嵌入式大气数据传感(FADS)系统,提出了基于容积卡尔曼滤波的惯性测量元件(IMU)数据和FADS数据融合算法。该算法对飞行器运动状态建立高阶滤波模型,使用容积点加权求和逼近的方法估计非线性运动模型,滤波输出值经处理后得到马赫数、攻角、侧滑角等大气数据。经仿真实验,算法计算的大气数据较为准确,马赫数误差小于0.01,攻角和侧滑角的误差小于0.1°。
Aircraft atmospheric data estimation is an important part of obtaining flight status, and it is the basis of achieving the stable control of aircraft.By studying the flush air data sensing(FADS) system, a data fusion algorithm for data inertial measurement unit(IMU) and FADS based on cubature Kalman filter is proposed.The high-order filtering model of aircraft motion state is established in this algorithm, and the cubature point weighted summation approximation method is used to estimate the nonlinear motion model.Mach number, angle of attack, sideslip angle and other atmospheric data are obtained by filtered output values.The simulation results show that the atmospheric data calculated by this algorithm is more accurate with Mach number error less than 0.01 and angle errors of attack and side slip less than 0.1°.
作者
肖地波
蒋保睿
刘鹏
XIAO Di-bo;JIANG Bao-rui;LIU Peng(School of Automation,Chengdu University of Information Technology,Chengdu 610225,China)
出处
《测控技术》
2022年第9期50-55,72,共7页
Measurement & Control Technology
基金
四川省科技计划(2020YFG0177)
四川省无人系统智能感知控制技术工程实验室开放课题(WRXT2021-004)。
