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Proceedings Paper

Fusion-based methods for target identification in the absence of quantitative classifier confidence
Author(s): James Llinas
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Paper Abstract

In an era of reduced defense budgets, there is increased pressure to reuse any available technology or capability to the extent possible. For data fusion applications, this requirement can lead to situations where the output of disparate individual algorithms would like to be fused; ideally, this would be done in the most quantitative way possible. This paper reviews, integrates, and comments on various prior works in both the data fusion, remote sensing, and character recognition communities which are helpful to the data fusion algorithm/process designer dealing, in particular, with target identification and classification problems. It is shown that generalized voting and rank-based methods may be useful in these cases; the issue of source reliability is also addressed and methods for incorporating assigned reliabilities are described.

Paper Details

Date Published: 28 July 1997
PDF: 12 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280831
Show Author Affiliations
James Llinas, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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