Visualization of graphical information fusion results

2015

SPIE Proceedings

Self Cite

The revision of the Joint Directors of the Laboratories (JDL) Information Fusion model in 2004 discussed information processing, incorporated the analyst, and was coined the Data Fusion Information Group (DFIG) model. Since that time, developments in information technology (e.g., cloud computing, applications, and multimedia) have altered the role of the analyst. Data production has outpaced the analyst; however the analyst still has the role of data refinement and information reporting. In this paper, we highlight three examples being addressed by the DFIG model. One example is the role of the analyst to provide semantic queries (through an ontology) so that vast amount of data available can be indexed, accessed, retrieved, and processed. The second idea is reporting which requires the analyst to collect the data into a condensed and meaningful form through information management. The last example is the interpretation of the resolved information from data that must include contextual information not inherent in the data itself. Through a literature review, the DFIG developments in the last decade demonstrate the usability of the DFIG model to bring together the user (analyst or operator) and the machine (information fusion or manager) in a systems design.

Section: Decision Supportmentioning

confidence: 97%

One decade of the Data Fusion Information Group (DFIG) model

2015

SPIE Proceedings

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“…Due to the lack of visual or auditory biometrics of the entities, the exact associations may be inaccurate. While the relational information may increase association accuracy [84,85], we pose that making hard association decisions may result in large inaccuracies in the situation assessments. Consequently, the situation reconstruction process must maintain and update uncertainties in the association between entities in different sensor feeds, and solve jointly the distributed reconstruction, association, and search problems.…”

Section: Entity Association In Distributed Scene Reconstructionmentioning

confidence: 99%

Issues and challenges of information fusion in contested environments: panel results

Kadar

Chong³

et al. 2015

SPIE Proceedings

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With the plethora of information, there are many aspects to contested environments such as the protection of information, network privacy, and restricted observational and entry access. In this paper, we review and contrast the perspectives of challenges and opportunities for future developments in contested environments. The ability to operate in a contested environment would aid societal operations for highly congested areas with limited bandwidth such as transportation, the lack of communication and observations after a natural disaster, or planning for situations in which freedom of movement is restricted. Different perspectives were presented, but common themes included (1) Domain: targets and sensors, (2) network: communications, control, and social networks, and (3) user: human interaction and analytics. The paper serves as a summary and organization of the panel discussion as towards future concerns for research needs in contested environments.

“…Currently, notional results and highlights from the use of the MiCRAFT probes and questions provide insights for verification and validation of multi-intelligence tools. We are building visualization techniques to display the various multiintelligence fusion results and allow the user a reconfigurable user-defined operating picture (UDOP) [107,108]. The tools should help build a narrative of the situation [109] as a work domain product.…”

Section: Basis Of Choicementioning

confidence: 99%

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

2015 National Aerospace and Electronics Conference (NAECON)

2015

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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 MultiIntelligence 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.