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Array Signal Processing

3EAH1 Array Signal Processing Electronics and Applied Physics S9
Cours : 15 h TD : 0 h TP : 15 h Projet : 0 h Total : 30 h
Responsable : Miloud Frikel
Pré-requis
Signal Processing, Digital signal processing
Objectifs de l'enseignement
This course presents some methods to exploit spatial diversity (a network of sensors) for the localisation and source separation. It helps to understand the principles of various algorithms of array processing. Implementation and a comparative study of these algorithms will be proposed in Labs. The study of these algorithms in the case of wideband signals will be processed.
Programme détaillé

Spatial filtering: beamforming
Parametric estimation: the principle of Capon, Levinson algorithms, Kumaresan, ...
Localisation of sources using high-resolution methods: MUSIC (subspace method), Minimum-Norm, ESPRIT, Propagator.
Localisation of correlated sources (spatial smoothing, frequency smoothing, processing in time domain ..)
Estimation in the presence of noise
Estimation of signal sources
Localisation of wideband sources : focusing operators
Applications to geolocation.
Applications (TD ou TP)
Applications for this course will be illustrated on geolocation systems.
We use Matlab / Simulink for these simulations. An example of applications to be carried out:

Localisation of radiating sources by beamforming
Detection of the number of radiating sources
Localisation by high-resolution methods (MUSIC, propagation)
Localization of wideband sources
localization in the presence of noise
Compétences acquises
Techniques of signal processing multi-sensors (array processing).
Bibliographie
S. Marcos (1998), Méthodes à haute résolution, traitement d'antenne et analyse spectrale, Hermès. Harry L. Van Trees (2002). Optimum Array Processing (Detection, Estimation, and Modulation Theory, Part IV. Wiley. Mark C. Sullivan (2008). Practical array processing. McGraw-Hill.

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