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<p>This paper proposes a method for estimating air data, including angle of attack (AoA), angle of sideslip (AoSS), Mach number, and static pressure, by integrating Flush Air Data Sensing (FADS), Inertial Navigation System (INS), and weather forecast data. The method applies to subsonic, transonic, supersonic, and hypersonic flight, supporting real-time and post-flight data processing. A filtering equation is developed using the FADS pressure model and flight kinematics, incorporating INS states and FADS measurements. The Cubature Kalman Filter (CKF) is used to fuse the data and estimate airspeed and ground speed, which are then converted into air data. Numerical simulations show that incorporating weather forecast information significantly reduces errors, with CKF outperforming the Extended Kalman Filter (EKF) in accuracy.</p>

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A Method for Estimating Aircraft Air Data by Fusing FADS/INS/Weather Forecast Information

  • 期刊论文

  • 发表日期：

    2025年4月10日

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  • DiBo Xiao,
  • JunJie Wang,
  • BaoRui Jiang

摘要

This paper proposes a method for estimating air data, including angle of attack (AoA), angle of sideslip (AoSS), Mach number, and static pressure, by integrating Flush Air Data Sensing (FADS), Inertial Navigation System (INS), and weather forecast data. The method applies to subsonic, transonic, supersonic, and hypersonic flight, supporting real-time and post-flight data processing. A filtering equation is developed using the FADS pressure model and flight kinematics, incorporating INS states and FADS measurements. The Cubature Kalman Filter (CKF) is used to fuse the data and estimate airspeed and ground speed, which are then converted into air data. Numerical simulations show that incorporating weather forecast information significantly reduces errors, with CKF outperforming the Extended Kalman Filter (EKF) in accuracy.