Asset Pairing for Battle Management

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7378
Agency Tracking Number: B122-004-0103
Amount: $149,938.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: MDA12-004
Solicitation Number: 2012.2
Small Business Information
Torch Technologies, Inc.
4035 Chris Drive, Suite C, Huntsville, AL, -
DUNS: 122515708
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mike Flaherty
 Principle Investigator
 (256) 319-6000
 mike.flaherty@torchtechnologies.com
Business Contact
 Kenneth Lones
Title: Director of Contracts
Phone: (256) 319-6019
Email: kenneth.lones@torchtechnologies.com
Research Institution
 Stub
Abstract
Torch proposes an innovative fusion of key technologies associated with an optimized asset pairing of BMDS sensors and weapon systems supporting enhanced interceptor utilization in complex multi-raid and multi-target environments. An optimized Sensor/Weapon Asset Pairing (SWAP) via a paired resource management process can provide for considerable BMDS performance enhancements and improved weapon system utilization, especially reduced interceptor leakage. SWAP algorithms and data fusion will seek to optimize Sensor Resource and Weapons Management in a synergistic fashion via a clear and logical definition of sensor and weapon constraints. Torch has chosen to make sensor constraint definition and processing a key focus of our Phase I real-time SWAP architecture via the incorporation of"Rule Based Constraint Satisfaction Programming"(RBCSP) logic within an information theoretic resource assignment process. The Torch approach leverages our significant experience with regard to the development of MDA SBIR funded real-time network based data fusion architectures for BMD. Our Phase I work plan focuses on the implementation and demonstration of a prototype real-time SWAP architecture via sensor, weapon, system, and feature based performance models coupled with highly efficient constraint based assignment algorithms in a network feedback parallel processing architecture.

* information listed above is at the time of submission.

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