Semantically Enriched Data for Effective Sensor Data Fusion

Geeth Ranmal De Mel* (Corresponding Author), Tien Pham, Thyagaraju Damarla, Wamberto Weber Vasconcelos, Timothy J Norman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

3 Citations (Scopus)


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),1 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.
Original languageEnglish
Title of host publicationProceedings of SPIE Vol. 8047
Subtitle of host publicationGround/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II
EditorsMichael A. Kodolny, Tien Pham, Kevin L. Priddy
Number of pages10
ISBN (Electronic) 9780819486219
Publication statusPublished - 8 Jun 2011
Event2011 Defense Security and Sensing - Orlando, United Kingdom
Duration: 25 Apr 201129 Apr 2011

Publication series

NameProceedings of SPIE
ISSN (Electronic)0277-786X


Conference2011 Defense Security and Sensing
Country/TerritoryUnited Kingdom

Bibliographical note

This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.


  • Knowledge Technologies
  • Semantic Web
  • Fusion
  • Publish/Subscribe
  • Reasoning Services


Dive into the research topics of 'Semantically Enriched Data for Effective Sensor Data Fusion'. Together they form a unique fingerprint.

Cite this