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

摘要

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.