利用飞行试验数据验证和修正地面气动力数据库，是飞行器设计与评估的一个重要环节。针对飞行试验普遍没有角加速度测量的情况，发展了一种新的气动系数辨识方法。首先将气动系数的时间导数建模为一阶Gauss-Markov过程，从而构建了气动系数辨识的数学模型。然后，从似然函数最大化出发，通过理论推导给出了初始状态期望值和协方差、过程噪声和测量噪声协方差等未知统计量的解析表达式。在此基础上，采用平方根无迹Kalman滤波器（SRUKF）和无迹Rauch–Tung–Striebel平滑器（URTSS），通过反复迭代给出未知统计量的最优估计，从而获得气动系数（作为增广状态变量）时间历程的辨识结果。两个飞机气动系数辨识算例演示了该方法的有效性。算例表明，该方法能够较好地估计未知统计量，给出合理的气动系数辨识结果。此外，该方法具有良好的收敛鲁棒性，不依赖于未知统计量的初始估计。

Verification and updating of the wind tunnel aerodynamic database using flight data is an important part of flight ve-hicle design and evaluation program. In response to the lack of angular acceleration measurement in flight tests, a novel aerodynamic coefficient identification method has been developed in this paper. Firstly, a mathematical model of aerodynamic coefficient identification issue was constructed by modeling the derivatives of aerodynamic coeffi-cient respect to time as first-order Gauss-Markov process. Then, analytical expressions for the optimal estimates of the unknown statistics, including the expectation and covariance of initial states and the covariances of process and measurement noise, were derived theoretically, by maximizing the likelihood function. On this basis, the unknown statistics were estimated by using the square root unscented Kalman filter (SRUKF) associated with the unscented Rauch-Tung-Striebel smoother (URTSS), through repeated iterations. Thereby, the time histories of aerodynamic coefficients, as the augmented state variables, were obtained. The effectiveness of the developed method was demonstrated by two examples of aircraft aerodynamic coefficient identification. It was shown that the unknown sta-tistics and aerodynamic coefficients could be estimated accurately. In addition, the method has good convergence robust, not relying on the initial estimates of the unknown statistics.