Cognitive Measures and Models for Persistent Surveillance

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$150,000.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-12-M-6323
Award Id:
n/a
Agency Tracking Number:
F121-023-2209
Solicitation Year:
2012
Solicitation Topic Code:
AF121-023
Solicitation Number:
2012.1
Small Business Information
3750 Palladian Village Drive, Building 600, Marietta, GA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
Y
Duns:
179321302
Principal Investigator:
CherylBolstad
Principal Research Associate
(770) 565-9859
cheryl@satechnologies.com
Business Contact:
RondaButler
Contracts Administrator III
(770) 565-9859
ronda.butler@satechnologies.com
Research Institute:
Stub




Abstract
ABSTRACT: The development of persistent wide area imagery surveillance systems for the Department of Defense is a huge step forwards for the Air Force intelligence capabilities. By providing continuous imagery updates across a broad field of view, these systems are expected to support both faster and more accurate warfighter decision-making. However, the human characteristics of the imagery analyst in terms of decision-making and information processing have not been factored into this equation. Our approach to this data overload and integration issue is to develop analysis techniques that take into consideration the task being performed, the amount of data to be analyzed, the information that needs to be extracted from the data, the required level of performance and the human capabilities to create adaptive analysis tools and techniques. By combining validated approaches of SA information analysis with human information processing theory and measures of performance (including impact of automation) we will create cognitive models that are reflective of the interaction between the ability of the warfighter and the varying task demands. These models can then be used to develop new and innovative guidelines and analysis approaches and techniques. BENEFIT: The TEAMS tools and techniques would be beneficial to organizations that utilize intelligence analysis, such as sensors, satellites and planes to support their operations. Within the military, this includes Tactical Operational Centers and intelligence analysts. Outside the military, potential customers include government organizations that support national security and disaster management, such as Federal Emergency Management Agency, Department of Homeland Security, and Centers for Disease Control and Prevention. These organizations and its employees must maintain global SA by continually processing large quantities of data to assess task-relevant aspects of the operational environment. By creating a analysis tool that has as its foundation domain-independent models of information processing, decision making, and automation theory the TEAMS tools and techniques are valid for many different domains.

* information listed above is at the time of submission.

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