NC-BSI: 3.3 Data Fusion for Decision SupportProblem Statement/Objectives:

Problem - Accurate situation awareness requires rapid integration of heterogeneous sources of information (e.g., sensors, human reports, etc.). Massive amounts of data must be transformed into usable information and knowledge for effective decision-making

Objectives: - Investigate methods to transform massive data into usable

information and knowledge as a resource for decision support and identify analytic tools and technologies for effective screening, surveillance and situational awareness.

- Develop techniques and methods for creating and supporting a Common Operating Picture (COP) at tactical, operational and strategic levels.

Methodology: - Develop methods for tasking diverse sensors and human

resources- Apply methods for heterogeneous data fusion- Create new methods to improve data interpretation and

visualization - Establish methods for creation, distribution and utilization

of a common operational picture

Benefits to DHS:• Improved situation awareness and coordination at all

levels from individual agents to DHS operations

• Demonstration of key technologies for data fusion and distributed situational awareness and decision making

• New techniques and methods for creating and supporting a Common Operating Picture (COP) at the tactical, operational and strategic levels

• New methods for effectively utilizing humans as “soft” sensors for inputs to situation reports

Deliverables and Timelines:1 Q: Development of arch./operational concepts 2 Q: Identify displays, tools, appropriate data sets

and appropriate subject matter experts 3 Q: Validate operational concept, SME4 Q: Experimental design / IRB- Year 2: Demonstration of COP displays and

algorithms (year 2)- Year 3-4: Experiments with humans-in-the-loop,

transition to 3.41

Energy Signals Data State vectors Labels KnowledgeEnergy Signals Data State vectors Labels Knowledge

NC-BSI: 3.3 Data Fusion for Decision SupportElevator speech:Fusion of multi-source data enables improved situational awareness and increased efficiency in decision making during high stakes decision making contexts.

New techniques will enable the transformation of massive data into information and knowledge for decision support.

This task will design and evaluate analytical tools, new visualization methods and conduct human-in-the-loop testing to support screening, surveillance and situational awareness.

Ongoing/leveraged research:

• Defense University Research Instrumentation Program (DURIP) – Extreme Events Laboratory ($ 200K)

• Rapid Reaction Technology Office (RRTO) - Understanding the Human Terrain ($ 430 K)

• Army Research Office (ARO) – Modeling & Mapping ($ 252 K)

Costs and Special Equipment:Year 1: $ 152,259Year 2: $ 159,150Year 3: $ 165,000Year 4: $ 140,000

Investigators:• David Hall, Professor of Information Sciences &

Technology; 814-867-2154, dhall@ist.psu.edu

• Isaac Brewer, Principal Scientist, Information Sciences & technology; 814-863-9467, isaacbrewer@psu.edu

• Jake Graham, Deputy Director NC2IF Center, jgraham@psu.edu

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MASINTHUMINTELINTCOMINTGEOINT

Leverage researchInfrastructure at

Penn State

NC-BSI: 3.4 Dynamic Resource Allocation Using Market-Based Methods

Problem Statement/Objectives:Problem - Multiple users simultaneously compete for

information resources such as sensors, communications networks and human observers in a dynamically changing environment.

Objectives:

• Develop techniques and methods for dynamic allocation of resources such as sensors, tasking human observers, and network communications for improved situational awareness.

• Link Information or knowledge needs to resources and adjudicate among conflicting user needs to improve situational awareness.

Methodology:

-Develop adaptive sensor management middleware that will compose and decompose task assignments into actionable sub-tasks.

- After developing models of human "sensors" (in Project 3.3), the market-based resource allocation system (3.4) will weigh task assignment tradeoffs such as safety, cost, or power consumption.

- Merge feedback from end users, from sensor performance measures, and from temporal pattern discovery to learn and to adapt its behavior over time.

Benefits to DHS:• Improved use of sensing, communications and human

resources for more accurate situational awareness and effective decision-making

• New sensor management architectures that effectively tasking fundamentally different types of sensing entities.

Deliverables and Timelines:-Begins years 4 and 5

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Elevator speech:• Conventional sensor networks are composed solely of

physical devices such as radar or infrared detectors, new technologies mean that networked data collection must consider novel "sensors" such as humans sending pictures via cell phone or intelligent software agents combing the web for information.

• Sensor management architectures must broaden their abilities to both express complex information gathering tradeoffs to users/decision makers, as well as effectively tasking fundamentally different types of sensing entities.

Ongoing/leveraged research: - Builds on findings from task 3.3 - Army Research Office – Center of

Excellence in Battlefield Sensor Fusion ($ 378 K)• Defense University Research

Instrumentation Program (DURIP) – Extreme Events Laboratory ($ 200K)

Costs and Special Equipment:

Year 5: $ 126,000Year 6: $ 124,000

Investigators:• Tracy Mullen, Penn State, Associate Professor of

Information Sciences & Technology; 814-865-6425, tam27@psu.edu

• David Hall, Professor of Information Sciences & Technology; 814-867-2154, dhall@ist.psu.edu

• Isaac Brewer, Research Associate, Information Sciences & Technology; 814-863-9467, isaacbrewer@psu.edu

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NC-BSI: 3.4 Dynamic Resource Allocation Using Market-Based Methods