| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Engineering | ||
| AUTOMATION ENGINEERING | ||
Course unit
ESTIMATION AND FILTERING
IN04119565, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ESTIMATION AND FILTERING
IN04119565, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
Information on the course unit
| Degree course |
Second cycle degree in AUTOMATION ENGINEERING IN0527, Degree course structure A.Y. 2008/09, A.Y. 2019/20 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 9.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | ESTIMATION AND FILTERING | |
| Department of reference | Department of Information Engineering | |
| E-Learning website | https://elearning.dei.unipd.it/course/view.php?idnumber=2019-IN0527-000ZZ-2019-IN04119565-N0 | |
| Mandatory attendance | No | |
| Language of instruction | Italian | |
| Branch | PADOVA | |
| Single Course unit | The Course unit can be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit can be chosen as Optional Course unit |
Lecturers
| Teacher in charge | STEFANO PINZONI | ING-INF/04 | |
|---|---|---|---|
| Other lecturers | GIANLUIGI PILLONETTO | ING-INF/04 |
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Core courses | ING-INF/04 | Automatics | 9.0 |
Course unit organization
| Period | Second semester |
|---|---|
| Year | 1st Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Lecture | 9.0 | 72 | 153.0 | No turn |
| Start of activities | 02/03/2020 |
|---|---|
| End of activities | 12/06/2020 |
| Show course schedule | 2019/20 Reg.2008 course timetable |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 9 A.A. 2018/2019 | 01/10/2018 | 15/03/2020 |
PINZONI
STEFANO
(Presidente)
PILLONETTO GIANLUIGI (Membro Effettivo) BEGHI ALESSANDRO (Supplente) BISIACCO MAURO (Supplente) CARLI RUGGERO (Supplente) CENEDESE ANGELO (Supplente) CHIUSO ALESSANDRO (Supplente) FERRANTE AUGUSTO (Supplente) FORNASINI ETTORE (Supplente) PICCI GIORGIO (Supplente) SCHENATO LUCA (Supplente) SUSTO GIAN ANTONIO (Supplente) TICOZZI FRANCESCO (Supplente) VALCHER MARIA ELENA (Supplente) VITTURI STEFANO (Supplente) ZAMPIERI SANDRO (Supplente) ZORZI MATTIA (Supplente) |
| Prerequisites: | Elements of signals and systems, data analysis, systems theory. |
| Target skills and knowledge: | To know and to be capable of using the principal methods of statistical signal processing, with applications to different fields of engineering.
In particular, at the end of the course, the student is expected: 1. to know and to use static probabilistic models for describing and estimating random variables and vectors; 2. to know and to use the stochastic process model for describing and estimating random signals; 3. to know and to apply dynamic estimation methods "à la Wiener"; 4. to know and to apply dynamic estimation methods "à la Kalman"; 5. to know and to use MATLAB to implement the estimation algorithms introduced in the course. |
| Examination methods: | The exam consists in 4 parts (total 32 pts, corresponding to the grade "30/30 e lode"), as follows:
1. Homework (2 pts): 4 exercises given at the end of the third week of course, on the subject of static estimation, to be returned within 1 week. 2. First MATLAB Laboratory (3 pts): an exercise about Wiener filtering, given at the end of the ninth week of course, report to be returned within 2 weeks. 3. Second MATLAB Laboratory (3 pts): an exercise about Kalman filtering, given at the end of the eleventh week of course, report to be returned within 2 weeks. 4. Written exam (24 pts): 4 exercises (static estimation, Wiener estimation, Kalman estimation, Markov chain Monte Carlo methods). Time 3 hours. |
| Assessment criteria: | Knowledge of the subject.
Capability to apply general results of Estimation Theory to solve conceptual and numerical problems. |
| Course unit contents: | Static and dynamic Bayesian estimation, minimum-error-variance unbiased linear estimators. Geometric approach.
Linear filters for signal estimation: predictors, reconstructors, and smoothers. Wiener-Kolmogorov filters. Spectral factorization and ARMA models. Minimum-variance stochastic control. State-space models. Kalman filter and its implementation. Extended Kalman filter. Bayesian estimation based on stochastic simulation: rejection/importance sampling, Markov chain Monte Carlo methods. Nonlinear filtering based on unscented and particle filters. Using MATLAB software. |
| Planned learning activities and teaching methods: | Traditional teaching with use of the blackboard. A few lectures about MATLAB in computer science laboratory. |
| Additional notes about suggested reading: | The course homepage on the E-learning platform at the website of the Department of Information Engineering will contain notes, complements, exercises, together with the lectures diary. |
| Textbooks (and optional supplementary readings) |
|
- Lecturing
- Interactive lecturing
- Questioning
- Loading of files and pages (web pages, Moodle, ...)
- Moodle (files, quizzes, workshops, ...)
- Latex
- Matlab
