Bayesian calibration using different prior distributions: an iterative maximum a posteriori approach for radio interferometers

Abstract : In this paper, we aim to design robust estimation techniques based on the compound-Gaussian (CG) process and adapted for calibration of radio interferometers. The motivation beyond this is due to the presence of outliers leading to an unrealistic traditional Gaussian noise assumption. Consequently , to achieve robustness, we adopt a maximum a poste-riori (MAP) approach which exploits Bayesian statistics and follows a sequential updating procedure here. The proposed algorithm is applied in a multi-frequency scenario in order to enhance the estimation and correction of perturbation effects. Numerical simulations assess the performance of the proposed algorithm for different noise models, Student's t, K, Laplace, Cauchy and inverse-Gaussian compound-Gaussian distributions w.r.t. the classical non-robust Gaussian noise assumption .
Type de document :
Communication dans un congrès
26th European Signal Processing Conference (EUSIPCO 2018), Sep 2018, Rome, Italy
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01851034
Contributeur : Remy Boyer <>
Soumis le : samedi 28 juillet 2018 - 20:35:46
Dernière modification le : jeudi 25 octobre 2018 - 11:39:43
Document(s) archivé(s) le : lundi 29 octobre 2018 - 12:24:18

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EUSIPCO 2018.pdf
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  • HAL Id : hal-01851034, version 1

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V Ollier, M Korso, A Ferrari, Remy Boyer, P. Larzabal. Bayesian calibration using different prior distributions: an iterative maximum a posteriori approach for radio interferometers. 26th European Signal Processing Conference (EUSIPCO 2018), Sep 2018, Rome, Italy. 〈hal-01851034〉

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