AESA-based RADAR Performance in Complex Sensor Environments
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. 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. LSI proposes to extend the work currently being performed under our N06-123 effort that is building a modular framework to develop and evaluate sensor resource management (RM) concepts. We will also leverage the results of our recently completed N05-006 effort entitled"Radar Detection and Discrimination of Small Maritime Targets at High Altitude and Grazing Angle that examined simultaneous multi-target tracking and discrimination (MTT & D) through the inclusion of target features within an interactive multiple model/multiple hypothesis tracking (IMM/MHT) framework . The connection between the RM and MTT & D lies in the desire to adaptively control sensor revisit and dwell time as needed, to maintain desired track quality and target discrimination. LSI will incorporate a MTT & D functional understanding and the associated performance attributes in the RM through the use of metadata. The overall proposed effort to develop and evaluate hybrid rule-based constrained optimization techniques for real-time adaptive optimization of airborne AESA and MSA control/scheduling involves many technical areas that are applicable to specific USN AESA radar sensors planned for the BAMS and VT-UAV platforms.
Small Business Information at Submission:
Lambda Science, Inc.
P.O. Box 238 Wayne, PA -
Number of Employees: