数据融合表征为在4个抽象级上进行:单元级、态势级、威胁级以及资源分配级。单元级特性试图确定各个平台目标属性。在态势级,探测诸如战斗群这样的单元编队。在威胁级和资源分配级,对可能的敌方意图和我方可能作出的反应进行推理。然而,对于更高级的抽象,也就是战斗力和威胁的抽象,没做什么工作。这篇论文提出了一种态势估计方法,其中包括战斗力与威胁的明确概念化。对这些战斗力与威胁的图形说明是为了加强单元级描述,避免含糊不清。这篇论文采用了一种崭新的用户接口——该接口允许人与计算机进行协作,使得战术估计优于人或机器独自工作的水平。论文随后说明人机责任的自然分工,并以战争情景为例说明这种协作系统的益处。 Data fusion is characterized by four levels of abstraction: unit level, situation level, threat level, and resource allocation level. Unit-level features try to determine the target attributes for each platform. At the state level, unit formations such as battle groups are explored. At the threat level and at the resource allocation level, the possible enemy’s intentions and the reactions that we may make are inferred. However, for a more advanced abstraction, the abstraction of combat effectiveness and threat, nothing was done. This paper presents a method for situation assessment that includes a clear conceptualization of combat effectiveness and threats. The graphical description of these combat effectiveness and threats is intended to reinforce the unit level description and avoid ambiguity. This paper uses a brand new user interface that allows people to collaborate with computers to make tactical estimates superior to those of humans or machines alone. The paper then describes the natural division of responsibility of man-machine responsibilities and illustrates the benefits of such a collaborative system using war scenarios as an example.