A careful selection of the best measured FRFs will generally produce much better global poles of the system for the modal parameter estimation process.

在模态参数估计过程中，仔细选择最好的测量频响通常会得到更好的系统全局极点。

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So firstly to get a better estimation of parameter using iterate inversion on wide scale, then using this estimation as initial value on mini scale till to get global optimum of original problem.

因此可先在粗尺度上迭代反演，得到一个较好的参数估计，再将这个估计作为较精细尺度的初值进行反演，直至原问题的全局最优解。

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From this point, the algorithm is implemented by searching and constructing a background image efficiently. With regard to global motion models, four-parameter estimation model is adopted.

算法从全局运动估计的基础出发，利用背景宏块运动相似性的特点快速建立背景宏块集合并采用常用的四参数全局运动估计模型估计运动参数。

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