Robust sparse time‐frequency analysis for data missing scenarios
Author:
Affiliation:
1. School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan China
2. School of Physics and Engineering Minnan Normal University Zhangzhou Fujian China
Funder
Minnan Normal University
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/sil2.12184
Reference50 articles.
1. Designing high-resolution time–frequency and time–scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance
2. Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances
3. Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation
4. Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation
5. Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain
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