Course Details for A.Y. 2013/2014

Course Objectives

The aim of the course is to introduce the principal techniques for the state estimation of stochastic linear discrete and continuous time systems and the most important algorithms for parametric identification of the dynamical systems.

Course Content

• Introduction
• Recall and complements of probability theory
• Minimum variance estimators
• Discrete-time stochastic systems
• Innovation sequences and related properties
• Kalman filter structure
• Kalman gain computation and simulation schemes
• Steady-state Kalman filtering
• Exended Kalman filter
• Indentificability of system parameters
• Maximum likelihood estimate
• Parametric identification of stochastic systems

Prerequisites and Learning Activities

Knowledge of the principles of the system and control theory and of the probability calculus.

Assessment Methods and Criteria

Oral examination

Textbooks

• M. Dalla Mora, A. Germani, C. Manes, Introduzione alla teoria dell'identificazione dei sistemi , Euroma. 1997. ISBN 88-8066-168-X    

Course page updates

• A.Y. 2012/2013
• A.Y. 2013/2014
• A.Y. 2014/2015
• A.Y. 2015/2016
• A.Y. 2016/2017
• A.Y. 2017/2018
• A.Y. 2018/2019
• A.Y. 2019/2020