Novel Planning Algorithms for Hybrid Land and Sea Platform Sensor Coordination

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$99,998.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
HQ0147-13-C-7375
Agency Tracking Number:
B122-001-0174
Solicitation Year:
2012
Solicitation Topic Code:
MDA12-001
Solicitation Number:
2012.2
Small Business Information
Technology Service Corporation
962 Wayne Avenue, Suite 800, Silver Spring, MD, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
053885604
Principal Investigator:
Geoffrey Rubin
Principal Staff
(540) 644-6836
geoff.rubin@tsc.com
Business Contact:
Joseph Mongeon
Dahlgren Division Manager
(540) 644-6817
joe.mongeon@tsc.com
Research Institution:
Stub




Abstract
TSC"s response to this Small Business Innovation Research (SBIR) topic demonstrates novel and innovative algorithms to fully address the objectives of this requirement. Our solution provides an adaptable approach that balances flexibility to include multiple sensor and engagement assets and the flexibility to apply the required level of fidelity to optimize BMD asset placement in a dynamic environment. The adaptable planning algorithms we will develop in this Phase I effort will provide the foundation for a comprehensive Mission Planner for a Hybrid AEGIS Ballistic Missile Defense (BMD) system comprised of both land-based and sea-based assets and will provide optimal Ship Operating Area (SOA) to defend a given area against missile raid. Our approach and design architecture will provide expandability to facilitate Phase II and Phase II+ follow-on efforts that will provide sensor coverage mapping, provide recommended Radar Search Doctrine (RSD) and resource/asset requirements regarding both radar and interceptors. This expandability will enhance the mission planning tool to further optimize engagement aspects of the BMD problem from fixed and mobile assets based on modeling data. Our solution will incorporate algorithmic performance models into the optimization to include realistic tracking uncertainties, lethality hypotheses, and to include studies that incorporate additional sensors (AN/TPY-2, THAAD and PTSS) into the optimization. Our algorithm design will use a proven Missile Defense System Simulation (MDSS) model. Our TSC developed National Missile Defense (NMD) Probability Analysis Tool (NPAT) will provide the basis for our MDSS and modeling to predict system performance. This is a proven tool that uses Monte-Carlo simulation.

* information listed above is at the time of submission.

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