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Sparse Time-Frequency decomposition for multiple signals with same frequencies

Thomas Y. Hou Caltech Zuoqiang Shi Tsinghua University

Numerical Analysis and Scientific Computing mathscidoc:1709.25004

Advances in Data Science and Adaptive Analysis, 9, (1750010), 2017
In this paper, we consider multiple signals sharing same instantaneous frequencies. This kind of data is very common in scienti c and engineering problems. To take ad- vantage of this special structure, we modify our data-driven time-frequency analysis by updating the instantaneous frequencies simultaneously. Moreover, based on the simul- taneously sparsity approximation and fast Fourier transform, some ecient algorithms is developed. Since the information of multiple signals is used, this method is very ro- bust to the perturbation of noise. And it is applicable to the general nonperiodic signals even with missing samples or outliers. Several synthetic and real signals are used to test this method. The performances of this method are very promising.
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[ Download ] [ 2017-09-27 09:23:41 uploaded by shizqi ] [ 630 downloads ] [ 0 comments ] 
@inproceedings{thomas2017sparse,
  title={Sparse Time-Frequency decomposition for multiple signals with same frequencies},
  author={Thomas Y. Hou, and Zuoqiang Shi},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170927092341852669823},
  booktitle={Advances in Data Science and Adaptive Analysis},
  volume={9},
  number={1750010},
  year={2017},
}
Thomas Y. Hou, and Zuoqiang Shi. Sparse Time-Frequency decomposition for multiple signals with same frequencies. 2017. Vol. 9. In Advances in Data Science and Adaptive Analysis. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170927092341852669823.
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