Number of hours
- Lectures 16.0
- Projects -
- Tutorials 10.0
- Internship -
- Laboratory works 12.0
- Written tests 2.0
ECTS
ECTS 0.5
Goal(s)
Study of the design of linear filters based on the minimization of the mean square error, with particular reference to adaptive implementations and the Kalman filter.
Content(s)
1 Introduction
2 Discrete stochastic processes
3 Wiener filtering
4 Linear prediction
5 Adaptive algorithms
6 Discrete Kalman filter
matrix calculation, probability and statistics, digital signal processing
40% contrôle continu + 60% examen terminal
Modalités d’examen terminal :
1 épreuve écrite – 2h
1 feuille A4 recto/verso manuscrite
Calculatrice autorisée
Sans téléphone, montre connectée, ordinateur
Tiers-temps : Durée identique avec note * 1,33
The course exists in the following branches:
- Curriculum - IESE - Semester 9
Course ID : KAIE9M23
Course language(s):
You can find this course among all other courses.
S. Haykin, Adaptive filter theory, Prentice Hall, 1991
B. Anderson, J. Moore, Optiimal filtering, Prentice Hall, 1979
C. Jutten, Filtrage linéaire optimal, notes de cours UGA, 2018