针对飞机颤振随机模型中输入输出观测数据带有观测噪声的问题，为得到较准确的飞机颤振模态参数，将辅助变量辨识方法与方差匹配方法相结合，形成一种新的辨识策略——辅助变量方差辨识方法. 在飞机颤振随机模型中，通过引入辅助变量来构造方差函数，导出最小化优化目标准则函数的求解过程，并详细地给出对应的偏
导式. 根据渐近分析理论，推导参数估计值的渐近方差矩阵表达形式. 利用此渐近方差矩阵不仅可以衡量辨识方法的有效性，而且可以设计最优激励信号. 将提出的方法用于飞行仿真转台电流环被控对象的传递函数辨识和飞机颤振模态参数辨识，验证了该方法的有效性.

 When the observed input-output data are corrupted by observed noise in an aircraft flutter statistic
model, one should obtain accurate parameters of the model. We combine the instrumental variable identification
method and covariance matching method to develop a new method: the instrumental variable covariance
method. In the statistic model of aircraft flutter, we introduce some instrumental variables to develop a covariance
function. We present the procedure to solve the criterion function and partial derivatives expression. We
derive the asymptotic covariance matrix expression obtained with the proposed method, and use the asymptotic
covariance matrix expression to verify effectiveness of the method, and design the external excitation
signal. The method is applied to identify the transfer function in the current loop of a flight simulator and
parameters of an aircraft flutter model. The simulation shows effectiveness of the method.