Semantically enriched data for effective sensor data fusion

Geeth de Mel; Tien Pham; Thyagaraju Damarla; Wamberto Vasconcelos; Tim Norman

doi:10.1117/12.885481

23 May 2011 Semantically enriched data for effective sensor data fusion

Geeth de Mel, Tien Pham, Thyagaraju Damarla, Wamberto Vasconcelos, Tim Norman

Proceedings Volume 8047, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II; 80470L (2011) https://doi.org/10.1117/12.885481
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Geeth de Mel, Tien Pham, Thyagaraju Damarla, Wamberto Vasconcelos, and Tim Norman "Semantically enriched data for effective sensor data fusion", Proc. SPIE 8047, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II, 80470L (23 May 2011); https://doi.org/10.1117/12.885481

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available