Details about this position are provided in the following **PDF.**

# Author: Jerome Bobin

## Analyzing multispectral radio-interferometric measurements on the sphere

Position: Internship Deadline: 28/02/2019 Contact: Jerome Bobin Details about this position are provided in the following PDF.

## Exemplar-based calibration for sparse inverse problems, with application to unmixing

## Component separation from multi-frequency radio-interferometric data, with application to the Epoch of Reionization (EoR) signal

Position: PhD Deadline: 31/05/2019 Contact: Jerome Bobin. Details about this position are provided in the following PDF.

## Blind separation of sparse sources in the presence of outliers

Authors: | C.Chenot, J.Bobin |

Journal: | Signal Processing, Elsevier |

Year: | 2016 |

Download: | Elsevier / Preprint |

## Abstract

Blind Source Separation (BSS) plays a key role to analyze multichannel data since it aims at recovering unknown underlying elementary sources from observed linear mixtures in an unsupervised way. In a large number of applications, multichannel measurements contain corrupted entries, which are highly detrimental for most BSS techniques. In this article, we introduce a new {\it robust} BSS technique coined robust Adaptive Morphological Component Analysis (rAMCA). Based on sparse signal modeling, it makes profit of an alternate reweighting minimization technique that yields a robust estimation of the sources and the mixing matrix simultaneously with the removal of the spurious outliers. Numerical experiments are provided that illustrate the robustness of this new algorithm with respect to aberrant outliers on a wide range of blind separation instances. In contrast to current robust BSS methods, the rAMCA algorithm is shown to perform very well when the number of observations is close or equal to the number of sources.