Error Analysis for Sparse Time-Frequency Decomposition of Non- Integer Period Sampling Signals
Year: 2019
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 1 : pp. 25–34
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.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22429
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 1 : pp. 25–34
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 10