A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition
Abstract
In order to detect the multi-component signal from the noise and chaos, a method based on the differential nonlinear mode decomposition (DNMD) is proposed in this paper. This new analysis approach applies the differential to the original signal. Then the nonlinear mode decomposition (NMD) is used to obtain a series of meaningful nonlinear modes, which has the advantage of extracting high frequency components with small amplitudes and learns from the superiority of NMD such as noise robust. Finally, the spectrum analysis is used to the decomposed components. The analysis of simulation signals and the real underwater signal is given to demonstrate the effectiveness of this method. Proposed method can detect multi-component signals of time-varying amplitude without fake frequency under the condition of noise and chaos. Compared with traditional decomposition methods, the peaks of Hilbert marginal spectrum of proposed method are sharper, and R2, R3 are higher.
Keywords
Multi-component signal detection, Differential nonlinear mode decomposition, Chaos, Spectrum analysis
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
T. Yang, J. Shao, Y. Huang and R. Malekian, "A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition," in Journal of Communications Software and Systems, vol. 14, no. 2, pp. 171-179, July 2018, doi: 10.24138/jcomss.v14i2.514
@article{yang2018methodmulti,
author = {Tiantian Yang and Jie Shao and Yue Huang and Reza Malekian},
title = {A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition},
journal = {Journal of Communications Software and Systems},
month = {7},
year = {2018},
volume = {14},
number = {2},
pages = {171--179},
doi = {10.24138/jcomss.v14i2.514},
url = {https://doi.org/10.24138/jcomss.v14i2.514}
}