Name
Title | ||
|---|---|---|
Complex Correntropy Applied to a Compressive Sensing Problem in an Impulsive Noise Environment |
Abstract | ||
|---|---|---|
Correntropy is a similarity function capable of extracting high-order statistical information from data. It has been used in different kinds of applications as a cost function to overcome traditional methods in non-Gaussian noise environments. One of the recent applications of correntropy was in the theory of compressive sensing, which takes advantage of sparsity in a transformed domain to reconstruct the signal from a few measurements. Recently, an algorithm called $\ell _{0}$ MCC was introduced. It applies the Maximum Correntropy Criterion (MCC) in order to deal with a non-Gaussian noise environment in a compressive sensing problem. However, because correntropy was only defined for real-valued data, it was not possible to apply the $\ell _{0}$ MCC algorithm in a straightforward way to compressive sensing problems dealing with complex-valued measurements. This paper presents a generalization of the $\ell _{0}$ MCC algorithm to complex-valued measurements. Simulations show that the proposed algorithm can outperform traditional minimization algorithms such as Nesterovs algorithm (NESTA) and the $\ell _{0}$ Least Mean Square ($\ell _{0}$ LMS) in the presence of non-Gaussian noise. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2947764 | IEEE ACCESS |
Keywords | DocType | Volume |
Compressed sensing,Signal processing algorithms,Kernel,Random variables,Calculus,Approximation algorithms,Sparse matrices,Complex correntropy,complex-valued data,compressive sensing,l0-approximation | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João P. F. Guimarães | 1 | 5 | 2.48 |
| Felipe B. Da Silva | 2 | 0 | 0.34 |
| Aluisio I. R. Fontes | 3 | 20 | 4.50 |
| Ricardo von Borries | 4 | 43 | 4.89 |
| Allan Martins | 5 | 25 | 7.15 |