Course Catalog 2013-2014
Basic

Basic Pori International Postgraduate Open University

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Course Catalog 2013-2014

ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr

Additional information

Suitable for postgraduate studies

Person responsible

Robert Piche

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 
 2 h/week
 2 h/week
+2 h/week
+2 h/week


 
ASE-5036 2013-01 Tuesday 12 - 14, SC203

Requirements

At least 66% of homework exercises, exam, and term project.
Completion parts must belong to the same implementation

Learning Outcomes

This course presents the methods to describe state information of a stochastic system and methods to update this information based on uncertain measurement data. Static and dynamic (both discrete and continuous time) systems. Student is capable of (grade (3/5) 1. To form the state estimate and estimate uncertainty for a system described with linear Gaussian model based on uncertain/incomplete data about state. 2. To present the principle of updating state information recursively for a Markov process; the principle of Bayes filter. 3. To construct a Kalman filter for linear Gaussian discrete time system. 4. To construct a Kalman filter for continuous time linear-Gaussian system, the state being measured at irregular intervals. 5. To construct dynamic validation algorithm for a linear static base function model (e.g. the calibration curve of a sensor). Grade (1/5): at least four of the goals achieved.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Gaussian distribution and conditional distributions derived from it.   Mutual information and Kullback-Leibler distance.  Fisher Information and Cramer-Rao inequality. 
2. Markov property and the resulting principle of updating state information recursively. Bayes filter.  Bias, uncertainty, consistency and efficiency of an estimate.  Kramers-Moyal equation 
3. Kalman filter for linear-Gaussian system.  Fokker-Planck equation.   
4. Solution of a linear stochastic differential equation; the dynamics of the state information.  Optimizing reference measurements.   
5. Kalman filtering of parameters of static linear (base function) model; dynamic model validation.   Extended Kalman filter. Particle filter.   

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   Bayesian Filtering and Smoothing   Simo Särkkä   9781107619289       Yes    English  

Prerequisites

Course Mandatory/Advisable Description
ASE-2510 Johdatus systeemien analysointiin Advisable    
ASE-5016 Advanced Methods of Data-driven Modelling and Analysis Advisable    

Additional information about prerequisites
Prerequisites are courses given in Finnish. Thus for this course it is sufficient to know the background in modeling and probability from any suitable course.

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr ACI-42136 Stochastic Estimation and Control, 5 cr  
ACI-21086 Control System Design with Matlab, 5 cr +
ACI-42066 Robust Control, 5 cr +
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr
ACI-42086 Optimal and Robust Control System Design with Matlab, 7 cr  
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
ASE-5036 2013-01 Lectures and exercises for the course.       Contact teaching: 0 %
Distance learning: 0 %
Self-directed learning: 0 %  

Last modified07.03.2014