Monday
11 a.m.-2 p.m. (Room A3 Via Ariosto 25)
Thursday
11 a.m.-1 p.m. (Room A7 Via Ariosto 25)
All students are required
to enroll in the GOOGLE CLASSROOM of Filtering and Optimal Control
for any kind of info, recorded lessons and any other item related to
the course
classroom code: Kadlymc
invitation link:
https://classroom.google.com/c/NjI1OTgyNjQ4NDJa?cjc=kadlymc
Basics
on probability theory: probability spaces, random vectors,
distributions and probability densities, expectations and conditional
expectations. Hilbert space of random vectors. Projection theorem.
Optimality
criteria: centering, consistency, efficiency. Rao-Cramer lower-bound
for error covariance.
Optimal estimation: weighted least square estimates, maximum
likelihood estimates and Bayesian estimates with minimum error
variance.
Recursive optimal estimators: Kalman filter. Steady state Kalman
filter. Kalman filter with correlated state and measurement noise.
Recursive weighted least squares, recursive parameter identification