A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time-frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

Automated summary

Complex Variational Mode Decomposition (CVMD) is an extension of the original VMD algorithm for analyzing complex-valued data. However, it faces difficulties in decomposing low-frequency signals and requires prior knowledge about the decomposition number. This paper proposes a modified method, MCVMD, which improves the accuracy of low-frequency signal decomposition and requires less prior knowledge. The effectiveness of the proposed algorithm is verified using both synthetic and real-world complex-valued signals.