Fusion-based methods for target identification in the absence of quantitative classifier confidence

James Llinas

doi:10.1117/12.280831

28 July 1997 Fusion-based methods for target identification in the absence of quantitative classifier confidence

James Llinas

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280831
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

James Llinas "Fusion-based methods for target identification in the absence of quantitative classifier confidence", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280831

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