Title :

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

Author(s) :

Ollier, V [Auteur]
Laboratoire des signaux et systèmes [L2S]
Systèmes et Applications des Technologies de l'Information et de l'Energie [SATIE]
Korso, M [Auteur]
Laboratoire Energétique Mécanique Electromagnétisme [LEME]
Ferrari, A [Auteur]
Observatoire de la Côte d'Azur [OCA]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire des signaux et systèmes [L2S]
Larzabal, Pascal [Auteur]
Systèmes et Applications des Technologies de l'Information et de l'Energie [SATIE]

Conference title :

26th European Signal Processing Conference (EUSIPCO 2018)

City :

Rome

Country :

Italie

Start date of the conference :

2018-09-03

English keyword(s) :

robustness
maximum a posteriori estimation
Bayesian calibration
compound-Gaussian distribution

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Planète et Univers [physics]/Astrophysique [astro-ph]

English abstract : [en]

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 ...
Show more >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 .Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Méthodes d'apprentissage pour les très grands réseaux d'antennes en radioastronomie

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL