Schedule
I will give a graduate course on Stochastic dynamic systems during
February  May 2009.
The course will start in midFebruary. Precise details will be
announced later on
this web page. The course will be given in English.
Literature
As a text we will use my book
T. Söderström: DiscreteTime Stochastic Systems 
Estimation and Control.
Second edition, SpringerVerlag, 2002.
Errata for
the second edition.
Prerequisites
The course should be appropriate for graduate students in automatic
control,
signal processing, systems theory, mathematical statistics, engineering
disciplines, physics, etc.
It is assumed that the participants have some basic working
knowledge in
stationary stochastic processes, linear algebra, and
discretetime dynamic systems.
A MSc (civilingenjör) in engineering physics or an equivalent
background from another
undergraduare program should be sufficient.
This also means that senior undergraduate students in their final year
can
sufficient background, and will be welcome to participate.
Organisation
There will be a session of 4 hours a week. Each session will include
Examination
The homework problems will be an essential part of the examination.
There will also be a final 'takehome' exam.
The homeworks assignments are organized into two parts, A and B.
Part A is mandatory, while the exercises in part B can be done by
students
who wants a deeped experience in the field (and more credit point).
The basic alternatives are thus
Registration
In order to plan the schedule for the course,
I would like interested participants to register for the course as soon
as possible,
and not later than by January 30, 2009.
You can do so by contacting me: phone (018183075), or email.
Please let me know if you have specific desires concerning
the schedule.
Course plan
Session  Week  Chapter  Topics  Homeworks 
1  8  13  Introduction. probability theory.
Complexvalued Gaussian variables. Stochastic processes. 

2  9  34  Second and higher order moments.
Linear filtering. 
A: 2:1, 3 B: 2: 4 
3 
10 
4 
State space representations. Linear filtering. Spectral factorization 
A: 3:3 B: 3:6 

11  4  Continuoustime models. Sampling.
Positive real part of the spectrum. 
A: 3: 1 B: 3: 4 

12  4  Bispectrum and filtering. Algorithms for covariance calculations. 
A: 4: 1, 2, 3 B: 4: 17 

13  5  Optimal estimation.  A: 4: 4, 8 B: 4: 6, 18 

14  6  Optimal state estimation of linear
systems (Kalman filters). 
A: 4: 12, 19 B: 4: 11 

16  7  Optimal estimation of linear systems
using a polynomial approach (Wiener filters). 
A: 5: 1, 3 B: 5: 2 

17  9  Nonlinear filtering. (Extended Kalman filtering, quantisation, medians). 
A: 6: 7, 8, 15 B: 6: 17, 20 

18  10  Optimal stochastic control. Dynamic programming. 
A: 7: 1, 9 B: 7: 3 

19  11  Linear quadratic Gaussian (LQG) control.
Duality. Controller design. 
A: 9: 3, 8 B: 9: 13 

20 
General discussion.  A: 10: 1, 4, 11: 5, 8 B: 10: 2, 11: 7 
Schedule and venue
The sessions are normally scheduled for Mondays 13.15  17.
Then venue is room P2345 (in house 2, MIC).
There are a few exceptions:
week 8: The session for this week is on Tuesday (February 17), 13.1517.
week 15: Note that there is likely no session this week.
week 16: The session for this week is on Tuesday (April 14), 13.1517.
week 21: Monday May 18 is booked as a reserve time.
OH slides in postscript:
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 9
Chapter 10
Chapter 11