A Topographic Kirchhoff Dynamic Focused Beam Migration Method Based on Compressed Sensing

Sun, Hui; Fang, Yang; Meng, Fanchang; Zhang, Zhihou; Gao, Cheng; Liu, Mingchen

doi:10.1109/access.2018.2873174

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…Due to this characteristic, CS has been successfully applied to many practical problems e.g. seismic prospecting [2], holographic microwave imaging [3], and health monitoring [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Complex Correntropy Applied to a Compressive Sensing Problem in an Impulsive Noise Environment

et al. 2019

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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 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 0 -MCC algorithm in a straightforward way to compressive sensing problems dealing with complex-valued measurements. This paper presents a generalization of the 0 -MCC algorithm to complex-valued measurements. Simulations show that the proposed algorithm can outperform traditional minimization algorithms such as Nesterov's algorithm (NESTA) and the 0 -Least Mean Square ( 0 -LMS) in the presence of non-Gaussian noise.

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“…Due to this characteristic, CS has been successfully applied to many practical problems e.g. seismic prospecting [2], holographic microwave imaging [3], and health monitoring [4]- [6].…”

Section: Introductionmentioning

confidence: 99%