Bayesian Tracking for Optimal Exploitation of A Priori Information

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$0.00
Award Year:
2001
Program:
SBIR
Phase:
Phase I
Contract:
F29601-01-C-0008
Award Id:
55162
Agency Tracking Number:
001NM-1988
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
500 West Cummings Park, Suite 3000, Woburn, MA, 01801
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
859244204
Principal Investigator:
ConstantinoRago
Research Engineer
(781) 933-5355
tino@ssci.com
Business Contact:
RamanMehra
President/CEO
(781) 933-5355
rkm@ssci.com
Research Institute:
n/a
Abstract
The Air Force is interested in critical technologies that will support beam control systems on airborne or space based high energy laser (HEL) weapons platforms used to engage both clear targets and targets that are viewed against background clutter.These include stationary and moving targets, tracking and adaptive optics using passive and active compensation, and fire control functions that include target acquisition and identification, and aimpoint selection. Air Force missions that are relevant tothis effort include Airborne Laser (ABL) main and adjunct missions, Tactical HEL Fighter (THELF) missions, applications of the Space Based Laser (SBL) and Relay Missions against ground and airborne targets, Airborne Tactical Laser (ATL) and Laser Gunshipmissions. During the course of this project, Scientific Systems Company Inc. (SSCI) and its partner Lockheed Martin-Eagan (LM-E), will address three objectives in support of these critical technologies. They are: 1) Fine Tracking in High ScintillationEnvironments (atmosphere distortion compensation), 2) Detection and Tracking of Ground Targets in Heavy Clutter Environments (track-before-detect techniques), 3) Test and Evaluation at AFRL/DEBA sites, the Advanced Concept Lab, MIT/LL, or other sponsoredsites and the transition of these technologies into AF missions. Scientific Systems has teamed up with Lockheed-Martin, Eagan for this project, and they will provide both technical and commercialization support. SSCI and LM-E have a strong background inthe areas of image analysis, pattern recognition and target tracking with applications to areas such as detection, tracking, and estimation in high clutter environments. These techniques are mathematically rigorous and are formulated within a recursiveBayesian nonlinear filtering approach to optimally exploit all available a priori information.

* information listed above is at the time of submission.

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