Level 5: user refinement to aid the fusion process

Blasch, Erik; Plano, Susan

doi:10.1117/12.486899

Cited by 58 publications

(39 citation statements)

References 5 publications

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“…Level 4 deals with the assignment of resources to tasks. Blasch (cf., [9]) proposed to add one more level -User Refinement -to this model. The goal of this additional level is to make an explicit connection between the computer processing (fusion) of information and human-in-the-loop.…”

Section: Situation Awarenessmentioning

confidence: 99%

A core ontology for situation awareness

Matheus¹,

Kokar

Bacławski

2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

179

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Section: Situation Awarenessmentioning

confidence: 99%

A core ontology for situation awareness

Matheus¹,

Kokar

Bacławski

2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

179

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“…These modes are similar to the User refinement behaviors of: neglect, consult, rely, and interact and over five basic functions of user refinement (1) Controlling, (2) Directing, (3) Coordinating, (4) Planning, and (5) Organizing [35].…”

Section: Team Decision Makingmentioning

confidence: 99%

Information fusion measures of effectiveness (MOE) for decision support

2011

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For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

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“…It was developed in 1985 by the U.S. Joint Directors of Laboratories (JDL) Data Fusion Group [1] with several recent revisions proposed [2], [3], [4].…”

Section: Jdl Data Fusion Modelmentioning

confidence: 99%

“…One of the more successful proposals for providing a framework and model for this broadened notion of data fusion is the U.S. Joint Directors of Laboratories (JDL) data fusion model [1] and its revisions [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

A knowledge processing middleware framework and its relation to the JDL data fusion model

Heintz

Doherty

2005

2005 7th International Conference on Information Fusion

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Abstract-Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose succesful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a knowledge processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses realtime CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the revised JDL data fusion model.

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Level 5: user refinement to aid the fusion process

Cited by 58 publications

References 5 publications

A core ontology for situation awareness

A core ontology for situation awareness

Information fusion measures of effectiveness (MOE) for decision support

A knowledge processing middleware framework and its relation to the JDL data fusion model

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