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AESA-based RADAR Performance in Complex Sensor Environments

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-08-C-0136
Agency Tracking Number: N062-123-0651
Amount: $750,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N06-123
Solicitation Number: 2006.2
Timeline
Solicitation Year: 2006
Award Year: 2008
Award Start Date (Proposal Award Date): 2008-02-13
Award End Date (Contract End Date): 2011-01-15
Small Business Information
P.O. Box 238
Wayne, PA 19087
United States
DUNS: 946893658
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Joseph Schanne
 Senior Systems Engineer
 (610) 581-7940
 jjschanne@lamsci.com
Business Contact
 Joseph Teti. Sr.
Title: Vice President
Phone: (610) 581-7940
Email: jteti@lamsci.com
Research Institution
N/A
Abstract

Many current United States Navy (USN) OPSIT/TACSIT scenarios comprise demanding dynamic environments for airborne sensors. The ability to task or mode interleave with adaptive scheduling is essential to achieving desired sensor/mission effectiveness. Both active electronically scanned array (AESA) and conventional mechanically scanned antenna (MSA) airborne multi-mode radar systems will require energy timeline resource management to realize their full performance capabilities. Furthermore, the programmable nature of many modern AESA based sensor architectures allows real-time modification of antenna pattern and waveform characteristics. Real-time adaptive optimization of AESA control and scheduling over the implicitly large number of degrees of freedom is computationally impractical without sufficient constraints. In contrast, non-adaptive legacy resource management approaches rely exclusively on rule-based constrained methodologies that relax computational concerns, but significantly under utilize radar energy timeline resources. Recent real-time computer systems research in optimum scheduling in complex dynamic environments has produced computationally efficient approximate solutions that are attractive for use with rule-based constraints. LSI proposes to develop and evaluate hybrid rule-based constrained optimization techniques for real-time adaptive optimization of airborne AESA and MSA control and scheduling. These techniques will apply to airborne AESA and MSA radar systems of interest to the USN and address both air-to-air and air-to-ground operation.

* Information listed above is at the time of submission. *

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