An Improved Time-Frequency Representation based on Nonlinear Mode Decomposition and Adaptive Optimal Kernel

Abstract

Time-frequency representation (TFR) based on Adaptive Optimal Kernel (AOK) normally performs well only for monocomponent signals and has poor noise robustness. To overcome the shortcomings of AOK TFR mentioned above, a new TFR algorithm is proposed here by integrating nonlinear mode decomposition (NMD) with AOK TFR. NMD is used to decompose multicomponent signals into a bundle of meaningful oscillations and then AOK is applied to compute the TFR of individual oscillations, finally all these TFRs are summed together to generate one TFR. Through quantitative comparison with other TFR methods to both simulated and real signals, the superiority of proposed TFR based on NMD and AOK on removing noise and many other measurement index of TFR are shown.

A Comparison of Time‐Frequency Signal Processing Methods for Identifying Non‐Perennial Streamflow Events From Streambed Surface Temperature Time Series

“…Using the resulting residuals, this procedure is repeated until a predefined stopping criterion is met, resulting in the estimation of a finite set of nonlinear modes. To date, the NMD approach has not been applied in hydrology but it has been used for signal processing of underwater acoustic and electrocardiogram signals (Xin et al., 2016). In the present study, the NMD approach was implemented using the Nonlinear Mode Decomposition Toolbox for MATLAB (Iatsenko et al., 2015).…”

An improved separation method of multi-components signal for sensing based on time-frequency representation

“…In order to obtain high time-frequency resolution and cross terms suppressed TFRs, a series of new time-frequency analysis methods are proposed in recent years. [6][7][8][9][10][11][12][13][14] When a TFR is obtained, the instantaneous frequencies (IFs) of signal components can be extracted in the time-frequency domain. For a monocomponent signal, its IF is denoted by a thin and energy concentrative curve called "ridge."…”

Modeling of Whole-Space Transient Electromagnetic Responses Based on FDTD and its Application in the Mining Industry

“…However, the horizontal component can also reflect some information on anomalous bodies, and joint interpretation with both the horizontal and vertical components can improve the precision of data interpretation. Some new techniques can also be applied to signal extraction and inversion of multi-component in the future [14][15]. In the multi-component study of TEM, [16] calculated the responses of both the vertical and the horizontal components from a conductive plate in half-space, and analyzed the different components' abilities of reflecting the anomalous body.…”