Vorlesung: Selected Aspects of Sensor Data Fusion – Methods and Applications

This lecture series is a follow-on of the lectures on "Introduction to Sensor Data Fusion - Methods and Applications" delivered in the winter semester. Based on the methods discussed and analyzed there, we proceed to methodological refinements and more advanced applications that directly lead us to the frontline of current research and innovative applications of Sensor Data Fusion. After a brief recapitulation of what we have learned so far, we will look into more sophisticated sensor data fusion approaches, such as target motion analysis, particle filtering, expectation-maximization methods, extended target tracking, probability hypothesis and intensity filtering, adaptive sensor management. We will understand power of these methods in their application to fusion problems taken from in recent research projects at Fraunhofer FKIE. Successful participation in the exercises of the previous lecture series "Introduction to Sensor Data Fusion" is required.

Weitere Informationen:

• Studienabschnitt: Master Computer Science, Hauptstudium
• Voraussetzungen: Introduction to Sensor Data Fusion (applies only for credit points and Vorlesungsschein)
• Fachbereich: (B,C) [B4]
• Aufwand: V2Ü2 (6CP)