基于噪声特性估计的气动系数辨识方法
收稿日期: 2024-07-09
修回日期: 2024-07-29
录用日期: 2024-09-24
网络出版日期: 2024-10-23
基金资助
国家级项目
Aerodynamic coefficient identification method based on noise statistics estimation
Received date: 2024-07-09
Revised date: 2024-07-29
Accepted date: 2024-09-24
Online published: 2024-10-23
Supported by
National Level Project
利用飞行试验数据验证和修正风洞气动力数据库，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过程噪声和测量噪声协方差等未知统计量的解析表达式。采用平方根无迹Kalman滤波器（SRUKF）和无迹Rauch-Tung-Striebel平滑器（URTSS）进行状态估计。根据状态估计结果显式计算未知统计量并迭代修正，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。2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。
关键词： 气动系数辨识; 噪声协方差; 平方根无迹Kalman滤波器; 无迹Rauch-Tung-Striebel平滑器; 似然函数; 飞行试验; 气动参数估计
汪清 , 郑凤麒 , 丁娣 , 岳茜 . 基于噪声特性估计的气动系数辨识方法[J]. 航空学报, 2025 , 46(7) : 130920 -130920 . DOI: 10.7527/S1000-6893.2024.30920
Using flight data to verify and update the wind-tunnel aerodynamic database is an important part of flight vehicle 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 was constructed by modeling the derivatives of aerodynamic coefficient respect to time as first-order Gauss-Markov process. Then， analytical expressions for the unknown statistics， such as the covariances of process and measurement noise， were derived theoretically by maximizing the likelihood function. The state estimation was conducted by using the Square Root Unscented Kalman Filter （SRUKF） associated with the Unscented Rauch-Tung-Striebel Smoother （URTSS）. The unknown statistics were computed explicitly and updated iteratively， based on the state estimation results. 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. The results showed that the unknown statistics and aerodynamic coefficients were estimated accurately. In addition， the method is of robust convergence with respect to the initial estimates of unknown statistics.
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