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

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
N0
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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
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

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2008 course timetable

Syllabus
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)
  • G. Picci, Filtraggio statistico (Wiener, Levinson, Kalman) e applicazioni. Padova: Lib. Progetto, 2007. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Interactive lecturing
  • Questioning
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure