Application of Advanced Techniques to Multi-INT Information Association and Fusion

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$739,504.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
FA8750-12-C-0085
Award Id:
n/a
Agency Tracking Number:
F103-050-1206
Solicitation Year:
2010
Solicitation Topic Code:
AF103-050
Solicitation Number:
2010.3
Small Business Information
10070 Barnes Canyon Road, San Diego, CA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
107928806
Principal Investigator:
Jameson Bergin
Principal Engineer
(703) 269-3620
jsb@isl-inc.com
Business Contact:
Nicole Sanese
Sr. Contract Administrato
(858) 373-2711
nsanese@islinc.com
Research Institution:
Stub




Abstract
ABSTRACT: Large numbers of intelligence, surveillance, and reconnaissance (ISR) sensors spanning a number of different phenomenologies (e.g., radio frequency and optics) are currently employed in support of the global war on terror. While these sensors provide data streams that contain a wealth of valuable information for the warfighter, the growing collection capability is effectively overwhelming intelligence analysts and systems which limits the amount of intelligence that can currently be extracted from existing sensor data. It is likely that opportunities to extract additional intelligence such as high-fidelity enemy movement patterns by fusing or associating data among disparate sensor types are being lost because tools do not exist to fuse the data without significantly increasing operator workload. Thus an opportunity exists to develop new analysis/fusion capabilities that can draw from diverse information sources and associate the data thus creating intelligence from previously unrelated products. BENEFIT: The primary commercialization path for the technology developed under the proposed effort is with Department of Defense program offices developing advanced ground station capabilities that exploit data from multiple ISR sensor sources. The new techniques developed under this SBIR will allow operators to focus more of their efforts on the intelligence extraction problem such as monitoring the activities of individuals of interest as opposed to spending their time on tedious tasks such as manual association of data among sensors to identify tracks of interest

* information listed above is at the time of submission.

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