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Global cost of assignment in the TBM framework for association of uncertain ID reports

机译:TBM框架中用于关联不确定ID报告的全球转让成本

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摘要

Suppose we are given multiple independent reports on the count and identification (class, type, allegiance) of objects that are present in a certain volume of interest. The reports are uncertain (random and non-specific) and collected with the probability of detection P_d < 1 and the probability of false alarm P_f > 0. The problem is to estimate the actual number of objects and their true identity. Adopting the formalism of the belief function theory as interpreted by the transferable belief model (TBM), a solution is proposed which maximises the plausibility of the global assignment cost of identification reports. The proposed global cost of assignment is shown by Monte Carlo simulations to outperform its ad hoc alternatives.
机译:假设我们获得了多个独立的报告,这些报告以一定的关注量显示了对象的数量和标识(类,类型,效忠)。报告是不确定的(随机的和非特定的),并且以检测概率P_d <1和错误警报概率P_f> 0收集。问题在于估计对象的实际数量及其真实身份。采用可转移信念模型(TBM)解释的信念函数理论的形式主义,提出了一种解决方案,该方案最大程度地提高了识别报告的全局分配成本的合理性。蒙特卡洛模拟显示了拟议的全球转让成本,胜过其临时替代方案。

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