融合气象观测数据的高精度大气数据估计算法研究

针对嵌入式大气数据观测系统（FADS）与惯性导航系统（INS）在计算飞行器大气数据时易受风速变化造成参数估计不准确的问题，文章提出一种融合FADS/INS/气象观测数据的大气数据解算方法。FADS依靠飞行器外表面的气压分布直接计算大气数据，INS提供姿态角与加速度，气象观测数据包括气压-高度的对应关系与风速矢量。融合过程结合FADS压力模型、飞行器运动模型和大气观测数据构建状态方程与观测方程，并采用滤波预处理、扩展卡尔曼滤波估计出精确的空速矢量和高度，结合INS数据进一步转换得到大气数据。仿真结果表明，文章提出的方法与现有未融合气象观测数据的算法相比，在攻角和侧滑角估计方面，估计误差降低30%，在马赫数估计方面误差降低了89%，在气压高度方面误差降低93%，估计精度得到有效提高。

High Precision Air Data Estimation Algorithm with Meteorological Observation

Aiming at the problem that the flush air data sensor system (FADS) and inertial navigation system (INS) are prone to inaccurate parameter estimation caused by wind speed changes when calculating aircraft air data. An air data solution method is proposed in this paper to integrate FADS/INS/meteorological observation data. The FADS calculates the air data based on the pressure distribution on the outer surface of the aircraft directly. The INS provides the attitude angle and acceleration. Meteorological observation data include the pressure altitude correspondence and wind speed vector. The fusion process combines the FADS pressure model, aircraft motion model and air data to build the state and observation equation, and uses filtering preprocessing and extended Kalman filter to estimate the precise airspeed vector and altitude, and further converts the INS data to obtain the air data. Simulation results show that the method proposed in the paper is effective in improving the estimation accuracy by reducing the estimation error by 30% in angle of attack and sideslip estimation, 89% in Mach estimation, and 93% in barometric altitude compared with the existing algorithms without fused meteorological observation data.