3EAH1 | Array Signal Processing | Electronics and Applied Physics | S9 | ||||||
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Cours : 15 h | TD : 0 h | TP : 15 h | Projet : 0 h | Total : 30 h | |||||
Responsable : Miloud Frikel |
Pré-requis | |
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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. |
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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 |
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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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