 Di Ruscio, D. (2002).Subspace system
identification.Theory and applications. Lecture notes
(pdffile).
 State estimation and the Kalman filter. Lecture notes
(pdffile). 21 pages. We skip sections 2.1, 2.3, 2.4 and
2.5.
 Further notes on the state
observer. MATLAB files: Function to find the gain,
K:
lpe2.m
lpe.m,
ss2ocf.m. Solution ex. 5.1:
ex_id_simp2.m File to generate input experiment:
prbs1.m
 Prediction error methods. Chapter 14 lecture notes.
(pdffile). Directly from this file: Ch. 14
 Di Ruscio, D. (2005). Subspace system identification
of the Kalman filter: Open and closed loop systems. Paper
 Di Ruscio, D (2001). An introduction to MATrix
LABoratory (MATLAB). Lecture
notes
 Additional support lecture notes on System
Identification, Ljung (1995)
 Development of System Identification. Ljung (1996).
 A view on the Unscented Kalman Filter (UKF) algorithm
and comparison with the Extended Kalman Filter (EKF). Paper

Closed and Open
Loop Subspace System Identication of the Kalman
Filter
The course syllabus is mainly taken from
enumerate: 1 and 5 (subspace system identification),
2 and 3 (state estimation and Kalman filter) and 4
(prediction error methods for system identification). See
also the Lecture Plan for details. Item 7 and 8 gives an
additional overview of the field of System identification.
For possible postscript files: Notes about how to read
post script (*.ps) files and to covert from psfiles to
pdffiles (use Gsview):
 Postscript (*.ps) files can be read and printed to
our post script printers by the gsview software. Note. A
new version of gsview is available and may be downloaded
from the internet.
Faglærer: Dr. ing., 1. amanuensis David Di
Ruscio
