计及随机噪声的频域多输入多输出模态参数识别

提出了一种计及随机噪声的多输入多输出频域模态参数识别方法。该方法基于连续时间域的频率响应函数有理分式模型,利用Forsythe正交多项式改善求解性态,属频域的极大似然类估计方法。在最小二乘估计的基础上,利用"先验"的随机噪声信息进行优化迭代的极大似然估计,得到更优的模态参数识别结果。根据Cramer Rao下界不等式,在几乎不增加计算量的情况下获得所识别结果的不确定性信息,提高了模态参数识别的可靠性。采用GARTEUR飞机模型作为仿真算例,对本算法进行了仿真验证。

Frequency Domain Multiple Input / Output Modal Parameters Identification with Consideration of Stochastic Noise

A multiple input/output frequency domain modal parameters identification method, which takes stochastic noise into account, is proposed. This method belongs to maximum likelihood class estimation series, and is based on the rational fraction model of continuous time domain frequency response function. Forsythe orthogonal polynomial is utilized to improve the numerical condition. First, the least square estimation is implemented to get the initial value of modal parameters. Then, with the "a priori" stochastic noise information, the optimization process of maximum likelihood estimation is carried out to get better identification result. Moreover, uncertainty information of the modal parameters is obtained, according to the Cramer-Rao low bounder inequality, almost without additional calculations. The GARTEUR aircraft model is taken to validate this algorithm.