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Semantic information fusion to enhance situational awareness in surveillance scenarios

 
: Müller, Wilmuth; Kuwertz, Achim; Mühlenberg, Dirk; Sander, Jennifer

:
Postprint urn:nbn:de:0011-n-4872498 (913 KByte PDF)
MD5 Fingerprint: efb075f6b3adee1a674cb99c78d7b32f
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Created on: 30.5.2019


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 : November 16-18, 2017, Daegu, Korea
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-6064-1
ISBN: 978-1-5090-6063-4
ISBN: 978-1-5090-6065-8
pp.397-402
International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) <2017, Daegu/Korea>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
data fusion; integration; intelligent system; military application; probabilistic logic; semantics; unmanned aerial vehicles (UAV)

Abstract
In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

: http://publica.fraunhofer.de/documents/N-487249.html