This paper considers the problem of signal decomposition and filtering by extending its scope to various signals that cannot be effectively dealt with existing methods. For the core of our methodology, we introduce a new approach, termed “ensemble patch transformation” that provides a framework for decomposition and filtering of signals; thus, as a result, it enhances identification of local characteristics embedded in a signal that is crucial for signal decomposition and designs flexible filters that allow various data analyses. In literature, there are some data-adaptive decomposition methods such as empirical mode decomposition (EMD) by Huang (Proc. R. Soc. London A 454:903–995, 1998). Along the same line of EMD, we propose a new decomposition algorithm that extracts essential components from a signal. Some theoretical properties of the proposed algorithm are investigated. To evaluate the proposed method, we analyze several synthetic examples and real signals.

本文通过将信号的范围扩展到不能用现有方法有效处理的各种信号来考虑信号分解和滤波的问题。作为方法论的核心，我们引入了一种称为“整体补丁变换”的新方法，该方法为信号的分解和过滤提供了框架。因此，结果增强了对信号分解中至关重要的信号中嵌入的局部特征的识别，并设计了可进行各种数据分析的灵活滤波器。在文献中，有一些数据自适应分解方法，例如Huang的经验模式分解（EMD）（Proc。R. Soc。London A 454：903-995，1998）。与EMD一样，我们提出了一种新的分解算法，该算法从信号中提取基本成分。研究了该算法的一些理论特性。为了评估所提出的方法，我们分析了几个综合实例和真实信号。