- LEPL1106 (or equivalent training in signals and systems)
- LEPL1108 (or equivalent training in probabilities and statistics)
At the end of this learning unit, the student is able to :
1.1; 1.2; 1.3
3.1; 3.2; 3.3
4.2At the end of this course, the students will be able to :
- Part 1 - Estimation: probability theory (reminder), Fisher and Bayesian estimation, bias, covariance, mean square error, Cramér--Rao bound, asymptotic properties, classical estimators (maximum likelihood, best linear unbiased, maximum a posteriori, conditional mean...), hidden Markov model, nonlinear filtering, particle filtering, Kalman filter.
- Part 2 - Stochastic Processes and LTI Filters: complex random variables, stochastic processes, stationarity, ergodism, autocovariance, power spectral density, transformation by LTI systems, white noise, spectral factorization, finite-dimensional models (AR, MA, ARMA...), Wiener filter.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Learning will be based on courses interlaced with practical exercise sessions (exercises done in class or in the computer room using MATLAB). In addition, the training includes a project to be realized by groups of 2 or 3 students.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Project during the course semester
- Other activities, such as quizzes and homework exercises, can be taken into account in the final grade
The evaluation will be written and individual.