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Proceedings Paper

Semantically enriched data for effective sensor data fusion
Author(s): Geeth de Mel; Tien Pham; Thyagaraju Damarla; Wamberto Vasconcelos; Tim Norman
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Paper Abstract

Data fusion plays a major role in assisting decision makers by providing them with an improved situational awareness so that informed decisions could be made about the events that occur in the field. This involves combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete, dependable etc.) than what it would have been if the data streams (hereinafter referred to as 'feeds') from the resources are taken individually. However, these feeds lack any context-related information (e.g., detected event, event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources. In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL), OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.

Paper Details

Date Published: 23 May 2011
PDF: 10 pages
Proc. SPIE 8047, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II, 80470L (23 May 2011); doi: 10.1117/12.885481
Show Author Affiliations
Geeth de Mel, Univ. of Aberdeen (United Kingdom)
Tien Pham, U.S. Army Research Lab. (United States)
Thyagaraju Damarla, U.S. Army Research Lab. (United States)
Wamberto Vasconcelos, Univ. of Aberdeen (United Kingdom)
Tim Norman, Univ. of Aberdeen (United Kingdom)


Published in SPIE Proceedings Vol. 8047:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II
Michael A. Kolodny; Tien Pham; Kevin L. Priddy, Editor(s)

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