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Bayesian Tracking for Optimal Exploitation of A Priori Information

Award Information

Department of Defense
Air Force
Award ID:
Program Year/Program:
2001 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
Phase 1
Fiscal Year: 2001
Title: Bayesian Tracking for Optimal Exploitation of A Priori Information
Agency / Branch: DOD / USAF
Contract: F29601-01-C-0008
Award Amount: $0.00


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.

Principal Investigator:

Constantino Rago
Research Engineer

Business Contact:

Raman Mehra
Small Business Information at Submission:

500 West Cummings Park, Suite 3000 Woburn, MA 01801

EIN/Tax ID: 043053085
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No