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中文摘要：
提出了一种基于自相关周期估计的经验模态分解（Empirical mode decomposition,EMD）端点延拓方法。在信号分解过程中通过高阶自相关函数估计各信号分量的周期,基于提出的整周期准则对信号分量进行数据延拓,得到的延拓数据符合信号分量的变化趋势。相对现有延拓方法,该方法解决了端点处数据跃变、波动趋势不一致的问题。该方法计算量小且效率高,适于周期或准周期、调频范围不大的信号。仿真结果验证了该方法的有效性。
英文摘要：
An extending method for empirical mode decomposition（EMD） is proposed based on self-correlation period estimation.The period of the signal components is evaluated through high-order self-correlation function in the process of the signal decomposition.Then,the signal components are extended based on the suggested entire period criterion.The trend of the produced sequence is in agreement with the original signal components.Compared with the existed extending method,the method solves the problem of data jumps at end points and disagreement with original signal trend.It is an efficient method with low complexity and is fit for periodic or quasi-periodic signal,and its frequency modulation range is narrow.Simulation results show the validity of the algorithm.
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