Error Analysis for Sparse Time-Frequency Decomposition of Non- Integer Period Sampling Signals

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Volume 14, Issue 1

Qi Yang

J. Info. Comput. Sci. , 14 (2019), pp. 025-034.

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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.

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@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 = {2019}, 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 = {3080-180X}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22429.html} }

TY - JOUR T1 - Error Analysis for Sparse Time-Frequency Decomposition of Non- Integer Period Sampling Signals AU - Qi Yang JO - Journal of Information and Computing Science VL - 1 SP - 025 EP - 034 PY - 2019 DA - 2019/03 SN - 14 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22429.html KW - AB - 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.

Qi Yang. (2019). Error Analysis for Sparse Time-Frequency Decomposition of Non- Integer Period Sampling Signals. Journal of Information and Computing Science. 14 (1). 025-034. doi:

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