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中文摘要：
提出一套适用于噪声环境的飞机颤振模态参数辨识方法。为减小噪声对辨识结果的影响，首先设计了一种针对扫频激励的时频滤波器，利用扫频信号及其响应在时频域分布较为集中的特点，有效去除噪声，提高了试验数据的信噪比。为进一步提高辨识精度。提出了一种基于随机模型的频域广义最小二乘辨识算法。将噪声条件下的系统辨识问题转化为广义整体最小二乘问题，并采用线性的广义奇异值分解求解模型系数，避免了非线性优化的复杂计算。通过优化加权项，获得了接近极大似然估计的辨识效果。最后，通过试飞试验数据验证了方法的有效性。
英文摘要：
The aim of this paper is to present a new method for flight flutter modal parameter identification in noisy environment. This method employs a time-frequency （TF） filter to reduce the noise before identification, which depends on the localization property of sweep excitation in TF domain. Then, a generalized total least square （GTLS） identification algorithm based on stochastic framework is applied to the enhanced data. System identification with noisy data is transformed into a generalized total least square problem, and the solution is carried out by the generalized singular value decomposition （GSVD） to avoid the intensive nonlinear optimization computation. A nearly maximum likelihood property can be achieved by ＇optimally＇ weighted generalized total least square. Finally, the efficiency of the method is illustrated by means of flight test data.
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