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Proceedings Paper

Statistical, biological, and categorical sensor fusion: an integrated methodology
Author(s): James Bonick; Christopher Marshall
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Paper Abstract

In this paper, we investigate sensor fusion along three avenues: statistical, biological, and categorical. The first two approaches are analyzed simultaneously to provide a precise and rigorous sensor fusion methodology. The statistical model currently enhances Bayesian methods for tracking, and suggests further application to target identification and fusion - involving both low level feature extraction and higher level sensor output combination. The biological model is also applied to multiple levels of the fusion problem. On the lowest level, it utilizes biologically-inspired results for improved feature extraction. On the higher levels, it develops biologically-inspired evolutionary and agency algorithms for sensor output combination and sensor network analysis. Ultimately, we model the entire fusion process with category theory. Category theory allows for the application of advanced mathematical theory to fusion analysis. In addition to using category theory as a modeling tool, in this paper we adapt categorical logic via topos theory to provide an advanced framework for decision fusion - initially using the topos of graphs. Graphs are a simpler representation. We suggest formulations which will be richer - toward the goal of a theoretically robust and computationally practical sensor fusion system for assisted/automatic target recognition.

Paper Details

Date Published: 17 April 2008
PDF: 9 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69680T (17 April 2008); doi: 10.1117/12.776701
Show Author Affiliations
James Bonick, U.S. Army RDECOM CERDEC NVESD (United States)
Christopher Marshall, U.S. Army RDECOM CERDEC NVESD (United States)

Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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