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Innovative approaches to Situation Modeling, Threat Modeling and Threat Prediction

Description:

TECHNOLOGY AREAS: Information Systems

OBJECTIVE:  Innovative approaches to Situation Modeling, Threat Modeling and Threat Prediction for improved Situational Awareness.

DESCRIPTION:  The Joint Directors of Laboratories (JDL) Subpanel on Data Fusion has defined Data Fusion as “a process dealing with the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats, and their significance.  The process is characterized by continuous refinements of its estimates and assessments, and the evaluation of the need for additional sources, or modification of the process itself, to achieve improved results”.

Steinberg, et al [Reference 1] later defined data fusion as “the process of combining data to refine state estimates and predictions.” A breakout of the functional levels [1] is:

• Level 0 - Sub-Object Data Assessment: estimation and prediction of signal/object observable states on the basis of pixel/signal level data association and characterization;

• Level 1 - Object Assessment:  estimation and prediction of entity states on the basis of observation-to-track association, continuous state estimation (e.g. kinematics) and discrete state estimation (e.g.  target type and ID);

• Level 2 - Situation Assessment:  estimation and prediction of relations among entities, to include force structure and cross force relations, communications and perceptual influences, physical context, etc.;

• Level 3 - Impact Assessment: estimation and prediction of effects on situations of planned or estimated/predicted actions by the participants; to include interactions between action plans of multiple players (e.g. assessing susceptibilities and vulnerabilities to estimated/predicted threat actions given one's own planned actions);

• Level 4 - Process Refinement (an element of Resource Management): adaptive data acquisition and processing to support mission objectives.

To date the majority of data fusion research, development, and applications focus primarily on the lowest levels of data fusion (e.g., Level 1 – Object Refinement).  The higher levels of information fusion (HLF) are inadequately being addressed, in particular the areas of Situation Modeling, Threat Modeling, and Threat Prediction.  This SBIR effort will therefore address these three domains, taking into account bias, uncertainty, and ambiguity within the data.

The specific area of research to be addressed for this topic is: research, development and application of novel methods to model and characterize the quality of data when it is reused for alternative purposes. This includes estimating the uncertainty or error in the resulting analysis due to the alternative data usage. The data that are available for reuse could include a mixture of quantitative/qualitative data types and are from structured and unstructured repositories or sources.

Impact: One could understand what is missing in the data and fill in the gaps by gaining a better understanding of how and why the original data were collected. Of particular interest are situations where data are gathered across different domains (physical, non-physical, cyber, medical, etc.) and are subsequently used for analysis in another domain.

PHASE I: The proposal for Phase I should identify an innovative approach for improving situational awareness through the use of novel methods to model and characterize the quality of data when it is reused for alternative purposes.  This includes estimating the uncertainty or error in the resulting analysis due to the alternative data usage. The data that are available for reuse could include a mixture of quantitative/qualitative data types and are from structured and unstructured repositories or sources.  The study should provide a detailed discussion on uncertain, incomplete and ambiguous data/information and how it is used in the Higher Level Fusion process.

PHASE II: In Phase II, development of a prototype Higher Level Information Fusion system based on the Phase I design.  Demonstrate the developed Higher Level Information Fusion prototype to prove feasibility for improving situational awareness by novel methods to model and characterize the quality of data when it is reused for alternative purposes through the development of novel methods to model and characterize the quality of data when it is reused for alternative purposes.

PHASE III Dual Use Applications: There are many dual use applications of Information Fusion techniques.  For example in the law enforcement community, this research could be applied to counter narcotics arena or Homeland Defense.  On the commercial side, this research is applicable to business intelligence, where companies attempt to determine what their competitors are doing by collecting and analyzing data available over the web.

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