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Distributed Perception Networks: An Architecture for Information Fusion Systems Based on Causal Probabilistic Models

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4 Author(s)
Gregor Pavlin ; Intelligent Autonomous Systems Group, Informatics Institute, University of Amsterdam. gpavlin@science.uva.nl ; Patrick De Oude ; Marinus Maris ; Thomas Hood

We introduce distributed perception networks (DPNs), a distributed architecture for efficient and reliable fusion of large quantities of heterogeneous and noisy information. DPNs consist of agents, processing nodes with limited fusion capabilities, which cooperate and can autonomously form arbitrarily large distributed classifiers. DPNs are based on causal models, which often facilitate analysis, design and maintenance of complex information fusion systems. This is possible because observations obtained from different information sources often result from causal processes which in turn can be modeled with relatively simple, yet mathematically rigorous and compact probabilistic causal models. Such models, in turn, facilitate decentralized world modeling and information fusion

Published in:

2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

Date of Conference:

Sept. 2006