Regularized maximum likelihood estimation for radio interferometric imaging in the presence of radiofrequency interefences

We consider a regularized Maximum Likelihood Estimation (MLE) framework to produce images in the context of radio interferometric measurements. Specifically, we consider the class of compound Gaussian distributions to model the additive noise in the presence of radiofrequency interferences. In most...

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Bibliographic Details
Published in:Signal processing 2024, Vol.220
Main Authors: Mhiri, Yassine, El Korso, Mohammed Nabil, Breloy, Arnaud, Larzabal, Pascal
Format: Article
Language:English
Subjects:
Astrophysics
Computer Science
Instrumentation and Methods for Astrophysic
Physics
Signal and Image Processing
Online Access:Get full text
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Description
Summary:We consider a regularized Maximum Likelihood Estimation (MLE) framework to produce images in the context of radio interferometric measurements. Specifically, we consider the class of compound Gaussian distributions to model the additive noise in the presence of radiofrequency interferences. In most cases, direct maximization of the likelihood is not tractable. To overcome this issue, we propose a generic expectation-maximization (EM) algorithm in the presence of a compound Gaussian noise. In addition, we leverage an approximation of the forward radio interferometric operator to derive an original latent data space that allows the use of the FFT in the maximization step, leading to an accelerated extension of the proposed imaging algorithm. The proposed approaches are evaluated on simulated and real data and show a significant improvement in the robustness to the presence of radiofrequency interferences (RFI) in the measurement.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2024.109430