Sensor Data Fusion - Trends in Methods and Applications

This video was recorded at IntelliSys 2016 -

The increasing trend in ubiquitous communication technologies coupled with decreasing costs for electronic components and hardware is leading to networked sensor systems which produce large quantities of data. As a branch of applied computer sciences, sensor data fusion addresses the ability to process this vast quantity of information in an effective and timely manner.
Methods of sensor data fusion are focused towards obtaining cognition, whereby information is intelligently processed to obtain situation awareness which can provide the basis for informed future decisions and actions.
Through high–performance algorithms, sensor data fusion is able to utilize both data correlations in time as well as complementary information which is gained by different sensors. By means of highly sensitive sensors, it is possible to estimate the state of one or multiple objects with a high accuracy even if they are spatial remote. Furthermore, the fusion process may integrate context information or expert knowledge in order to improve the results.
Within the past decade, novel techniques for Bayesian state estimation have been published. This talk is providing an overview on recent state–of–the–art tracking and estimation algorithms.

Felix Govaers received his Diploma in Mathematics in 2007 and his PhD in Computer Science in 2012, both at the University of Bonn, Germany. Since 2009 he works at Fraunhofer FKIE in the department for Sensor Data Fusion and Information Fusion where he leads the research group “Distributed Systems”. His research interest is focused on data fusion for state estimation in sensor networks. This includes track–extraction, processing of delayed measurements as well as the Distributed Kalman filter and non–linear filtering.
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