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Proceedings Paper

Sensor Fusion: Storage, Search And Problem Solving Efficiency Issues Associated With Reasoning In Context
Author(s): Richard Antony
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Paper Abstract

This paper proposes an abstract model of the data fusion process that is based on a generalization of the sensor correlation paradigm. The fusion model demonstrates that sensor fusion is a proper subset of data fusion and reveals two fundamental approaches to individual fusion processes based on explicit and implicit representations of knowledge. Most attempts to automate data fusion have been based on various forms of explicit representation, while implicit representations are more characterisitic of knowledge representations employed by human problem solvers. The efficiency of the two approaches is contrasted for two specific fusion problems: doctrinal templating and path planning. While an explicit representation approach may require the exhaustive representation of a large number of explicit templates, an implicit representation approach ( which supports reasoning in context) may require the maintenance, search and manipulation of large domain knowledge bases. A previously proposed database organization is recommended that supports the fusion process for both representation classes by facilitating highly efficient mixed semantic and spatial-oriented queries and manipulation.

Paper Details

Date Published: 1 March 1990
PDF: 13 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.970009
Show Author Affiliations
Richard Antony, CECOM Center for Signals Warfare (United States)


Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

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