Paper Info
Title
Dynamic Data Driven Applications System Concept For Information Fusion
Abstract
We present a framework to Information Fusion (IF) using the Dynamic Data Driven Applications Systems (DDDAS) concept. Existing literature at the intersection of these two topics supports environmental modeling (e.g. terrain understanding) for context enhanced applications. Taking advantage of sensor models, statistical methods, and situation-specific spatio-temporal fusion products derived from wide area sensor networks, DDDAS demonstrates robust multi-scale and multi-resolution geographical terrain computations. We highlight the complementary nature of these seemingly parallel approaches and propose a more integrated analytical framework in the context of a cooperative multimodal sensing application. In particular, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between IF and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements - an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computation remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and identification. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science
Year
DOI
Venue
2013
10.1016/j.procs.2013.05.369
2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE
Keywords
Field
DocType
Information Fusion, DDDAS, Cooperaive Sensing, Wide-Area Motion Imagery, target tracking, situation awareness
Data mining,Parallels,Situation awareness,Computer science,Terrain,Dynamic data,Artificial intelligence,Wireless sensor network,Information fusion,Machine learning,Piecewise,Computation
Conference
Volume
ISSN
Citations 
18
1877-0509
6
PageRank 
References 
Authors
0.51
12
3
Name
Order
Citations
PageRank
Erik Blasch1105190.91
Guna Seetharaman258444.59
Kitt Reinhardt381.30