Visualizing Uncertainty for Data Fusion Graphics: Review of Selected Literature and Industry Approaches

Abstract

The objective of this review is to better understand the current state-of-the-art methods for representing uncertainty in sensor-fusion data using graphical, visual displays to inform military decision making. Decision makers in Maritime defence and security often consider many sources of information when trying to develop an understanding of the situation within their area of responsibility. Information from each source has some uncertainty, as does their integrated or "fused" summary result. The purpose of presenting uncertainty is to enhance understanding of where the contact is, has been and will be so that the operator is able to make better decisions about the contacts intent. The scientific literature presents a number of approaches to graphical information visualization that improve comprehension of uncertainty using intrinsic or extrinsic modification of graphical elements. However, little of the existing research shows this understanding results in improved decision making in the Maritime domain. Few commercial vendors of display systems provide uncertainty estimates in their tactical plots and it is not clear whether this feature would be useful to tactical Maritime operators. This report suggests a Human Factors analysis approach to clarify where uncertainty visualization may be useful in Maritime defence and security, followed by recommendations of extrinsic and intrinsic uncertainty visualization techniques that could be used to validate the analysis experiment.

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Document Details

Document Type
Technical Report
Publication Date
Jun 09, 2015
Accession Number
AD1004347

Entities

People

  • Brad Cain
  • Jaff Guo
  • Tab Lamoureux

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Command And Control
  • Computational Science
  • Computer Graphics
  • Data Mining
  • Detection
  • Detectors
  • Human Factors Engineering
  • Information Science
  • Information Systems
  • Machine Learning
  • Ontologies
  • Pattern Recognition
  • Psychology
  • Supervised Machine Learning

Readers

  • Artificial Intelligence
  • Database Systems and Applications
  • Sensor Fusion and Tracking Systems.