Statistical signal and data processing

Information

Teacher coordinator	Romain Hérault
Teacher(s)	Romain Hérault
Language	French
Credits	2.5
Teaching	Lectures : 10.5h Exercises : 10.5h Practical work : 10.5h
Web site	https://moodle.insa-rouen.fr/course/view.php?id=1159

Aim and objective

Introduction to random signal and time series
Caraterize the main properties of random signal
Explain and describe the notion of parametric signal models and the main linear signal models and their estimation

Outcome learning

INSA reference data :
- Filtrer et modéliser des signaux [3P]
- Optimiser un modèle [3P]
CNISF reference data :
- J10Q [1P]
- H30T [2P]

1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

Introduction to statistical signal
Stationarity and non-stationarity properties
Statistical description random signals (mean, auto-correlation, covariances)
Linear signal models (AR, ARMA) and their least-squares estimation
Introduction to state space representation and notion of Kalman filter
Applications.

Prerequisites

Signal processing
Matlab/Python programmation

Bibliography

Therrien C. W. and M. Tummala, Probability and Random Processes for Electrical and Computer Engineers, Second edition, CRC Press, 2011
M. Barret, Traitement statistique du signal, Technosup, Ellipses, 2009.

Assessment

Final exam: 70%,
Laboratorey work-Project: 30%