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

机译：在没有定量分类器置信度的情况下基于融合的目标识别方法

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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.!13
机译：摘要：在国防预算减少的时代，越来越大的压力要求尽可能重用任何可用的技术或能力。对于数据融合应用程序，此要求可能会导致需要融合各种单独算法的输出的情况;理想情况下，这将以尽可能最定量的方式完成。本文对数据融合，遥感和字符识别社区中的各种现有工作进行了回顾，整合和评论，这有助于数据融合算法/过程设计人员处理特别是目标识别和分类问题。结果表明，在这些情况下，通用投票和基于等级的方法可能会有用。源可靠性的问题也得到解决，并描述了合并指定可靠性的方法。！13

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