Results of the Lecture Evaluation

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For those students who have more than 50% of the points from the exercises, an oral examination is offered. The examinations will take place on the following two days:
13th February, after 14:00
14th February, before 13:00
The examination will take place in room II.10 (Institute of Computer Science 4,
Friedrich-Ebert-Allee 144)
A list of Matrikel-Numbers of all students who are permitted to register: 

  • 2208115
  • 2328904
  • 2372474
  • 2353023
  • 2206228
  • 2383292
  • 2206092
  • 2073198
  • 2115635

You may register for a time slot by sending us an email to now!

Both 13th and 14th of February correspond to the first try exams. The second try will take place on 30th of March.

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
  • Follow-up/Side-events: The lecture will be continued in SS 2012.

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


Information on exams and exercisesIntroduction_SDF_Lecture.pdf
Slides Lecture  1 - 19.10.2011SDF__Lecture_1__WS_11-12.pdf
Slides Lecture  2 - 26.10.2011SDF__Lecture_2__WS_11-12.pdf
Slides Lecture 3 - 02.11.2011SDF__Lecture_3__WS_11-12.pdf
Slides Lecture 4 - 16.11.2011SDF__Lecture_4__WS_11-12.pdf
Slides Lecture  5 - 16.11.2011SDF__Lecture_5__WS_11-12.pdf
Slides Lecture  6 - 23.11.2011SDF__Lecture_6__WS_11-12.pdf
Slides Lecture  7 - 30.11.2011SDF__Lecture_7__WS_11-12.pdf
Slides Lecture  8 - 14.12.2011SDF__Lecture_8__WS_11-12.pdf
Slides Lecture  9 - 21.12.2011SDF__Lecture_9__WS_11-12.pdf
Slides Lecture 10 - 11.01.2012SDF__Lecture_10__WS_11-12.pdf

Slides Lecture 11 - 18.01.2012

Slides Lecture 12 - 25.01.2012

Slides Lecture 13 - 01.02.2012








Exercises 1 - 26.10.2011Exercises_SDF_1.pdf
Exercises 2 - 02.11.2011Exercises_SDF_2.pdf
Exercises 3 - 09.11.2011Exercises_SDF_3.pdf
Exercises 4 - 16.11.2011Exercises_SDF_4.pdf
Exercises 5 - 23.11.2011Exercises_SDF_5.pdf (updated on 24.11.2011)
Exercises 6 - 30.11.2011Exercises_SDF_6.pdf
Exercises 7 - 21.12.2011Exercises_SDF_7.pdf
Exercises 8 - 11.01.2012Exercises_SDF_8.pdf
Exercises 9 - 18.01.2012Exercises_SDF_9.pdf


Solutions to exercises 3SDF__Lecture_3__WS_11-12__exercises.pdf
Excursus Lecture 6SDF__Lecture_6__WS_11-12__exercises.pdf

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 filter