Name
Abstract | ||
|---|---|---|
Much effort has been devoted to recovering sparse signals from one-bit measurements in recent years. However, it is still quite challenging to recover signals with high fidelity, which is desired in practical one-bit compressive sensing (1-bit CS) applications. We introduce the notion of Schur-concavity in this paper and propose to construct signals by taking advantage of
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, which are capable of enhancing sparsity. Specifically, the Schur-concave functions can be employed to measure the degree of concentration, and the sparse solutions are obtained at the minima. As a representative of the Schur-concave family, the normalized
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Shannon entropy function (
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-SEF) is exploited. The resulting optimization problem is nonconvex. Hence, we convert it into a series of weighted
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-norm subproblems, which are solved iteratively by a generalized fixed-point continuation algorithm. Numerical results are provided to illustrate the effectiveness and superiority of the proposed 1-bit CS algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2925606 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Signal processing algorithms,Entropy,Noise measurement,Minimization,Atmospheric measurements,Particle measurements,Compressed sensing | Journal | 67 |
Issue | ISSN | Citations |
16 | 1053-587X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |