Sensor fusion for situation awareness in littoral environments

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: DAAH0103CR008
Agency Tracking Number: 02SB2-0027
Amount: $98,636.00
Phase: Phase I
Program: SBIR
Awards Year: 2002
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
Aetion Technologies Llc
1275 Kinnear Road, Columbus, OH, 43212
DUNS: 158053434
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 John Josephson
 Founding Partner
 (614) 340-1835
Business Contact
 Tom Slavin
Title: President
Phone: (614) 340-1835
Research Institution
"To maximize situational awareness, while minimizing cognitive overload, automated abductive inference (best-explanation reasoning) will be used to create a changing, "best interpretation" representation of the situation from incoming data. Modeling andsimulation will be used by the abductive inference software for automatic generation of predictions from hypotheses, enabling the continual generation of predictions to support: hypothesis evaluation, sensor tasking, planning, and detection of anomaliesthat may be valuable indications of deception, modeling errors, or sensor failure. Abductive inference will work tightly with predictive infererence to to provide a reliable, self-correcting representation of the situation, based on current evidence fromsensor data, using domain knowledge encoded as causal-model fragments.Aetion proposes to extend its current technology base to create software for building and composing sensor-fusion applications that are modular and extensible as new types of sensors are integrated, and as new knowledge is available about object types,sensor characteristics, and causal processes that mediate the effects of objects on sensors. If this is feasible, the resulting software should be cost effective and highly valuable for multiple sensor fusion applications, resulting in systems able tosqueeze more usable information from less data than systems not using causal relationships and domain models. Our automated i

* information listed above is at the time of submission.

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