2018
DOI: 10.1016/j.sigpro.2018.07.024
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Robust distributed calibration of radio interferometers with direction dependent distortions

Abstract: In radio astronomy, accurate calibration is of crucial importance for the new generation of radio interferometers. More specifically, because of the potential presence of outliers which affect the measured data, robustness needs to be ensured. On the other hand, calibration is improved by taking advantage of these new instruments and exploiting the known structure of parameters of interest across frequency. Therefore, we propose in this paper an iterative robust multi-frequency calibration algorithm based on a… Show more

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Cited by 21 publications
(14 citation statements)
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“…Similar to earlier theoretical work we have not considered the non-Gaussian nature of the noise discussed by Kazemi & Yatawatta (2013); Ollier et al (2017) and (Ollier et al 2018). In this work we study the impact of the variance as the PS only sensitive to that.…”
Section: Discussionmentioning
confidence: 89%
“…Similar to earlier theoretical work we have not considered the non-Gaussian nature of the noise discussed by Kazemi & Yatawatta (2013); Ollier et al (2017) and (Ollier et al 2018). In this work we study the impact of the variance as the PS only sensitive to that.…”
Section: Discussionmentioning
confidence: 89%
“…2) Compound-Gaussian noise: To generalize this scenario, in this paper we relax this hypothesis and we consider a colored compound-Gaussian (CG) noise as for instance it is standard in high resolution radar detection [19]- [21], in MIMO radar target localization [5] or in astronomical robust imaging [29,30]. The CG distribution family has many applications and is quite often used, for instance, to model the radar clutter in which targets and other interferences are embedded.…”
Section: ) Random Tadmentioning
confidence: 99%
“…In the context of radio astronomical imaging, aside from thermal noise, low-rank terrestrial interferers [8] are imminent, which adversely impact the imaging quality, especially for subspace-based methods in general and estimators under the PR framework in particular [9,10]. Therefore, deriving robust DOA estimators under consideration of the non-Gaussianity of both interference and noise [11][12][13] is of great interest. In this paper, we propose a robust DOA estimator under the consideration of non-Gaussian observations which does not rely on the robust estimation of the covariance matrix as in [8,[14][15][16][17].…”
Section: Introductionmentioning
confidence: 99%