Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT) method

Erik Blasch
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引用次数: 10

Abstract

Assessment of multi-intelligence fusion techniques includes algorithm performance credibility, mission needs quality assessment, and work-domain usability. Situation awareness (SAW) bridges low-level information fusion (tracking and identification), with high-level information fusion (threat and scenario-based assessment), against user refinement (physical, cognitive, and information tasks). To measure SAW, common techniques include the NASA TLX (Task Load Index), SAGAT (Situational Awareness Global Assessment Technique) probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points. These SAW tools are combined for a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measure SAW in the QuEST of information.
多智能关键等级评估融合技术(MiCRAFT)方法
多智能融合技术的评估包括算法性能可信度评估、任务需求质量评估和工作领域可用性评估。态势感知(SAW)将低级信息融合(跟踪和识别)与高级信息融合(威胁和基于场景的评估)结合起来,对抗用户细化(物理、认知和信息任务)。为了测量SAW,常用的技术包括NASA TLX(任务负荷指数)、SAGAT(态势感知全球评估技术)探针、SART(态势感知评级技术)问卷调查和CDM(关键决策方法)决策点。这些SAW工具被用于融合技术的多智能关键评级评估(MiCRAFT)。重点是通过信息融合服务质量(QoS)度量度量用户对情况的细化:及时性、准确性、置信度、工作负载(成本)和注意力(吞吐量)。任何用户分析的关键组成部分包括数据的相关性、关联和汇总;因此,我们也在信息探索中寻求测量声呐。
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