@Article{JICS-14-025,
author = {Qi Yang},
title = {Error Analysis for Sparse Time-Frequency Decomposition of Non- Integer Period Sampling Signals},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {14},
number = {1},
pages = {025--034},
abstract = { In  this  paper,  we  review  a  nonlinear  matching  pursuit  approach  (Hou  and  Shi,  2013),  a  data-
driven  time-frequency  analysis  method,  which  is  looking  for  the  sparsest  representation  of  multiscale  data 
over a dictionary consisting of all intrinsic mode functions (IMFs). In many practical problems, signals are 
non-integer period sampled. In other words, the time window may not contain exactly an integer number of 
signal periods. We consider the sparse time-frequency decomposition of non-integer period sampling signals 
by the nonlinear matching pursuit method and estimate the error. The estimation show that the relative error 
depends on the separation factor, the frequency ratio, and the number of periods of the IMF. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22429.html}
}