Important Information

Die Prüfungstermine haben sich gegenüber BASIS geändert:

  • 1. Prüfungstermin: 8. August, ab 15:00 Uhr
  • 2. Prüfungstermin: 5. September, ab 15:00 Uhr
  • Raum: LBH, II.10

Sie können ab jetzt einen Zeit-Slot registrieren. Schreiben Sie hierzu bitte eine Email an Felix Govaers.


Es wird eine zusätzliche Vorlesung mit einer umfangreichen Wiederholung des Vorlesungsstoffes am 6. August von Hr. Dr. Koch geben.

Lecture: Einführung in die Sensordatenfusion

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: Bachelor
  • Requirements:
  • Faculty:
  • 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

Job Offer

The department offers a position for a Bachelor thesis. If you are interested in enhancing algorithms for state estimation problems, please contact us.


The slides and other downloads are available from within the university or via passwort. The password is announced or can be requested at



Additional Information

Journal Tutorial published by P.D. Dr. W. KochIEEE_AESS_Tutorial_V__Koch.pdf
An introduction to the Kalman filter

Scala Tutorial