Paper
6 August 2002 Distributed data fusion over an ad hoc network
Sean Anderson, Lewis A. Binns, Peter R. C. Collins, Andrew Cooke, Phil Greenway, Dimitris Valachis
Author Affiliations +
Abstract
We have been developing a decentralised architecture for data fusion for several years. In this architecture, sensing nodes, each with their own processing, are networked together. Previously, we have researched fully connected networks, tree-connected networks, and networks with loops, and have developed a range of theoretical and empirical results for dynamic networks. Here we report the results obtained from building and demonstrating a decentralised data fusion system in which the nodes are connected via an ad hoc network. Several vision based tracking nodes are linked via a wireless LAN. We use UDP to establish local routing tables within the network whenever a node joins, and TCP/IP to provide point to point communications within the network. We show that the resulting data fusion system is modular, scalable and fault tolerant. In particular, we demonstrate robustness to nodes joining and leaving the network, either by choice or as a result of link drop-out. In addition to experimental results from the project, we present some thoughts on how the technology could be applied to large scale, heterogeneous sensor networks.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean Anderson, Lewis A. Binns, Peter R. C. Collins, Andrew Cooke, Phil Greenway, and Dimitris Valachis "Distributed data fusion over an ad hoc network", Proc. SPIE 4741, Battlespace Digitization and Network-Centric Warfare II, (6 August 2002); https://doi.org/10.1117/12.478713
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data fusion

Algorithm development

Image processing

Sensors

Cameras

Computer architecture

Detection and tracking algorithms

Back to Top