- General Information -

The examination dates for the lecture are:

Friday 15th of February starting at 9am and

Tuesday 26th of May starting at 9am.

The students with the following Matrikel-Nr. have succeeded in the exercises and may register for an examination time slot. Please write an email to lectureSDF@REMOVETHISPART.fkie.fraunhofer.de.




Job Offer

The department offers a position for a undefinedDiploma/Masterthesis (text in german only). If you are interested in enhancing our Oracle based Airtraffic Monitoring System with sophisticated statistical analysis as well as the automatic detection of phases of flight (e.g. waiting loops), please contact us.

Lecture: Introduction to Sensor Data Fusion - Methods and Applications

Sensor data fusion is an omnipresent phenomenon that existed prior to its technological realization or the scientific reaction on it. In fact, all living creatures, including human beings, by nature or intuitively perform sensor data fusion. Each in their own way, they combine or fuse sensations provided by different and mutually complementary sense organs with knowledge learned from previous experiences and communications from other creatures. As a result, they produce a mental picture of their individual environment, the basis of behaving appropriately in their struggle to avoid harm or successfully reach a particular goal in a given situation. Sensor Data Fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying real-world phenomenon is achieved. Typically, this insight is either unobtainable otherwise or a fusion result exceeds what can be produced from a single sensor output in accuracy, relia- bility, or cost. Appropriate collection, registration and alignment, stochastic filtering, logical analysis, space-time integration, exploitation of redundancies, quantitative evaluation, and appropriate display are part of Sensor Data Fusion as well as the integration of related context information. Today, Sensor Data Fusion is evolving at a rapid pace and present in countless everyday systems and civilian products.




  • Term: Master, Diploma (Graduate)
  • Requirements:
  • Faculty: MA-INF 3310, (B,C)[B4]
  • Effort: 2L+2E
  • Exams: Information on the exams can be found in the Introduction Slides which are offered for download below. If you have further questions, please contact felix.govaers@fkie.fraunhofer.de.
  • Follow-up/Side-events: The lecture will be continued in SS 2013.

The slides and other downloads are available from within the university or via passwort. The password is announced will be announced at the next lecture or can be requested at lectureSDF@fkie.fraunhofer.de


24.10.2012undefinedLecture 1
31.10.2012undefinedLecture 2
07.11.2012undefinedLecture 3
14.11.2012undefinedLecture 4
21.11.2012undefinedLecture 5 + undefinedExercises
28.11.2012undefinedLecture 6
12.12.2012undefinedLecture 7
19.12.2012undefinedLecture 8
09.01.2013undefinedLecture 9
16.01.2013undefinedLecture 10
23.01.2013undefinedLecture 11
30.01.2013undefinedLecture 12

Additional Information

Journal Tutorial published by P.D. Dr. W. KochIEEE_AESS_Tutorial_V__Koch.pdf
Monograph by P.D. Dr. W. KochTracking_and_Sensor_Data_Fusion__Koch.pdf
An introduction to the Kalman filterhttp://www.cs.unc.edu/~tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf