Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

View Researcher's Other Codes

MATLAB code for the paper: “Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Yiyuan She, Jiangping Wang, Huanghuang Li, and Dapeng Wu
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio.
Date of publication 2013
Code Programming Language MATLAB
Comment

Copyright Researcher 2021