Unified Bayesian Global ISR

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$99,974.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
D11PC20090
Award Id:
n/a
Agency Tracking Number:
10SB3-0041
Solicitation Year:
2010
Solicitation Topic Code:
SB103-003
Solicitation Number:
2010.3
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, -
Hubzone Owned:
N
Minority Owned:
Y
Woman Owned:
N
Duns:
859244204
Principal Investigator:
Adel El-Fallah
Sr. Group Leader-Tracking&Fusion
(781) 933-5355
adel@ssci.com
Business Contact:
Jay Miselis
Corporate Controller
(781) 933-5355
contracts@ssci.com
Research Institution:
Stub




Abstract
Automated construction of a unified global ISR pictureone that optimizes theater-wide tactical priorities, while permitting ongoing operator contextual guidancepresents a daunting theoretical and practical challenge. First is the huge variety of seemingly incommensurable information sources and information types. Second, automatic global ISR algorithms must compute optimal solutions that are based on evolving priorities, and thus also adaptively and dynamically determine the best spatial and temporal deployments of all sensors and platforms in order to maximize relevant information. Ultimately, this means that global ISR requires a seamless and optimal integration of: multisensor-multitarget data fusion; multitarget search, detection, localization, identification, and tracking; multitarget-multisensor sensor management; tactical prioritization; and operator contextual guidance. The Scientific Systems Company, Inc. team proposes a foundational, information-theoretic and control-theoretic approach to global ISR. Our approach is based on five innovations: (1) a multisensor-multitarget likelihood function and multisource Markov density that encapsulate all relevant information regarding the characteristics of the various sensors and platforms; (2) dynamic"tactical importance functions (TIFs) that semi-automatically specify the evolving meaning of"target of interest (ToI), and which are the mathematical portals through which operator contextual guidance can be inserted; (3) an information-theoretic, and yet intuitively meaningful objective function, the posterior expected number of targets of interest (PENTI); and (4) integration of these concepts with approximate multitarget filters (specifically, multitarget-moment filters or multi-hypothesis correlator trackers). The project team includes Dr. Ronald Mahler of Lockheed Martin. Lockheed Martin will provide both technical and commercialization support in the application of the advanced Global ISR Algorithms.

* information listed above is at the time of submission.

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