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Sparse time-frequency decomposition for multiple signals with same frequencies. (English) Zbl 1378.94007

Summary: In this paper, we consider multiple signals sharing the same instantaneous frequencies. This kind of data is very common in scientific and engineering problems. To take advantage of this special structure, we modify our data-driven time-frequency analysis by updating the instantaneous frequencies simultaneously. Moreover, based on the simultaneous sparsity approximation and the Fast Fourier Transform, we develop several efficient algorithms to solve this problem. Since the information of multiple signals is used, this method is very robust 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 demonstrate the robustness of this method. The performances of this method seems quite promising.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)

Software:

PDCO
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Full Text: DOI arXiv

References:

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