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基于非线性系统高阶近似的思想，提出一种比推广卡尔曼滤波（ＥＫＦ）更接近非线性系统本质的近似滤波方法，并应用于飞行状态的参数估计（或称为飞行轨迹重构）问题。仿真和实际飞行数据计算结果表明：提出的非线性近似滤波方法比ＥＫＦ有更高的估计精度和更好的鲁棒性，对飞机机动形状、数据长度要求不高，滤波收敛速度快。利用飞行状态估计数学模型的具体特点，使计算量和存储量大幅度减少。该方法应用于非线性较强的飞行状态及参数估计问题。可得到比ＥＫＦ更好的结果。
Based on the idea of high-order approximation in nonlinear systems, an approximate filtering method that is closer to the essence of nonlinear systems than extended Kalman filter (EKF) is proposed and applied to the estimation of flight state parameters (or flight path reconstruction) . Simulation results and actual flight data show that the proposed nonlinear approximate filtering method has higher estimation accuracy and better robustness than EKF. It requires less maneuver shape and data length and has faster convergence rate. The use of flight state estimation mathematical model of the specific features, so that the amount of computation and storage significantly reduced. The proposed method is applied to the non-linear flight state and parameter estimation. Get better results than EKF.