Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction

Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction

Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditio...

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Bibliographic Details
Main Author: Vedran Jurdana
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Technologies
Subjects:
time–frequency distribution
sparse reconstruction
regularization parameter
deep learning
neural network
compressive sensing
Online Access:https://www.mdpi.com/2227-7080/12/12/251
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