Module Name Kalman & Levinson Filtering
Module Code EE618
Module Co-ordinator Selim Solmaz
Department Electronic Engineering
Module Level 5
Credit rating 7.5 ECTS credits
Pre-requisites Basic knowledge of linear algebra, linear system analysis and probability
Co-requisites Linear algebra
Aims To develop an expertise in stochastic systems and filtering theory for
applications in estimation and control problems.
Learning Outcomes At the end of this module the student will:
Get the basic mathematical background on stochastic processes and
discrete time system analysis
Understand the projection theorem and use it to derive various concepts
in control theory
Learn about the derivation and the applications of Kalman filters as
optimal state estimators
Familiarize with Levinson filters and reduced order state estimation
Time Allowance for Constituent Elements
Lectures 36 hours
Tutorials 0 hours
Class Tests 0 hours
Homework Assignments (8 x 6hr) 48 hours
Independent study 36 hours
Discrete time stochastic processes and state space models
Wide sense stationary processes
Spectral analysis and the spectral density
The Levinson filter, inverse scattering and prediction
Homework Assignments (8) 100 %
Penalties: Late submission of assignment will not, in general, be accepted.
Pass Standard and any Special Requirements for Passing Modules: In order to pass this module,
students must achieve at least 40% in combined continuous assessment components.
Requirements for Autumn Supplemental Examination: The continuous assessment mark is carried
forward to the Autumn examinations as there is no facility available for repeating the continuous
assessment components of the course.
Continual Assessment Results: Assignments will be corrected within two weeks after submission,
provided that this does not extend past the end of the semester. Solutions and corrected scripts will be
available for viewing upon request.
The homework assignments include each specific topic learned during the course to achieve the
aforementioned learning outcomes. By working on the assignments individually or in groups, students get
the extensive working experience of the topics covered in class. The final grade is based on continuous
assessment and is calculated from the cumulative grades of the individual assignments.
Course Text Complete lecture notes will be handed out.
References Any book on the topics covered can be used as a reference textbook
Some suggestions are:
Introduction to Random Signals and Applied Kalman Filtering, Brown &
Hwang, 3rd Edition, Wiley, 1997
Kalman Filtering, Grewal & Andrews, 2nd Edition, Wiley Inter-science
Programmes currently utilizing module
Masters in Electronic Engineering