Signal and Image processing


The activities of the SPECTRAL team focus on developping statistical methods for signal and image processing, tailored by different applications. The common thread of our work is the resolution of detection/estimation issues from imperfect sensor data. A special attention is paid to exploiting at most the available information on the quantities of interest.

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Signal research team

Presentation of scientific activities

The difficulty lies in the diversity of this information which can concern the structure of the unknown variables or the data, their orders of magnitudes or, in a dynamic context, the way they evolve over time.

On the one hand, the objective is to propose relevant models to represent the information at hand. On the other hand, part of our work consists in developping optimal estimators based on these models along with algorithms to calculate them. Three main applications have been considered so far : localization and tracking, digital communications and biomedical engineering.

Localization/navigation and tracking

In navigation, a vehicle calculates its own position from embedded sensors whereas tracking consists in an external sensor which computes the trajectories of a set of targets. In both cases, the issue is to estimate a set of parameters which vary through time and represent the dynamics of a set of mobiles. Different issues may arise but the team is more particularly interested in the following topics. 

  • study of metrics to assess the similarity of different models,
  • in the case of maneuvers, Bayesian non parametric models to allow the mobiles to switch between an a priori unlimited number of dynamic models,
  • taking intrinsically into account constraints. For instance, in vision based localization, the pose of the camera lies in a Lie group.
  • use of copula to represent spatial correlations between the different satellite measurements,
  • Bayesian non parametric models to deal with the strong multimodality of the error in an urban context.
  • developing algorithms to improve the detection in the presence of clutter and thermal noise.
  • Improving the range/Doppler profiles through time-frequency analysis.
  • Tracking algorithms based on random finite sets theory.
Spectral team

Biomedical engineering : proteomics

A new generation of clinical research methods, based on proteomic analysis, is currently emerging either for diagnosis or pronostic. The objective is to characterize some diseases by specific signatures on protein profiles. Two main issues are : the early detection of deseases and an individualized treatment. They are based on the discovery of biomarkers. A for these issues, from a data processing point of view, one of the challenges is to develop a model selection method.

We work in a bayesian framework which leads to optimal selection functions. They rely on the computation of evidences, which is a well-known difficult task. We present analytical expressions, eiher exact or approximated. In the end, we take into account both technological and biological variabilities. We study their impact on the performance of the biomarker discovery methods (type I and type II errors).  This is a crucial step in the development and exploitation of these analysis chains.

Digital communications

New communication standards are more and more stringent as for the spectral and energetic efficiency of the communicating systems. In this context, our contributions deal with massive multi-antenna systems 


Spectral Skills

Bayesian inference

optimal filtering

time-frequency analysis

source detection and localization

Monte Carlo estimation

model selection

inverse problems


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Meet the members of the research team

Romain TAJAN
Laétitia JADOT
Guillaume FERRE
Résumé en français

Les activités de l’équipe SPECTRAL se concentrent sur le développement de méthodes statistiques pour le traitement des signaux et des images, adaptées à différentes applications. Le fil conducteur de notre travail est la résolution des problèmes de détection/estimation à partir de données de capteurs imparfaites. Une attention particulière est accordée à l’exploitation maximale des informations disponibles sur les quantités d’intérêt.

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