The Hilbert-Huang transform (HHT) is a classic method in time-frequency analysis field which was proposed in 1998. Since it is not limited by signal type, it is generally applied in medicine, target detection and so on. Empirical mode decomposition (EMD) is a pre-processing part of HHT. However, EMD still has many imperfect aspects, such as envelope fitting, the endpoint effect, mode mixing and other issues, of which the most important issue is the mode mixing. This paper proposes a mode mixing suppression algorithm based on self-filtering method using frequency conversion. The proposed algorithm focuses on the instantaneous frequency estimation and the false components removing procedures, which help the proposed algorithm to update or purify the designated intrinsic mode function (IMF). According the simulation results, the proposed algorithm can effectively suppress the mode mixing. Comparing with ensemble empirical mode decomposition (EEMD) and mask method, the suppression performance is increased by 26%.

中文翻译：

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基于自滤波方法的经验模态分解的模态混合抑制算法

Hilbert-Huang变换(HHT)是1998年提出的时频分析领域的经典方法。由于它不受信号类型的限制，因此普遍应用于医学、目标检测等领域。经验模式分解 (EMD) 是 HHT 的预处理部分。然而，EMD仍有许多不完善的方面，如包络拟合、端点效应、模式混合等问题，其中最重要的问题是模式混合。本文提出了一种基于变频自滤波方法的混模抑制算法。所提出的算法侧重于瞬时频率估计和虚假成分去除程序，这有助于所提出的算法更新或净化指定的本征模式函数（IMF）。根据仿真结果，该算法能有效抑制模式混合。与集成经验模式分解（EEMD）和掩模方法相比，抑制性能提高了26%。