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Object Avoidance Tactical Decision Aid (OATDA)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N65538-04-M-0134
Agency Tracking Number: N041-086-0944
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N04-086
Solicitation Number: 2004.1
Timeline
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-05-13
Award End Date (Contract End Date): 2005-07-11
Small Business Information
40 Lloyd Avenue, Suite 200
Malvern, PA 19355
United States
DUNS: 075485425
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 W. Monach
 Vice President
 (757) 727-7700
 reynolds@va.wagner.com
Business Contact
 W. Monach
Title: Vice President
Phone: (757) 727-7700
Email: reynolds@va.wagner.com
Research Institution
N/A
Abstract

Daniel H. Wagner Associates, Inc. will develop an Object Avoidance Tactical Decision Aid (OATDA) that processes all available data, dynamically generates a CTP and an object avoidance plan, and displays this information on the ship's navigation system. A key capability provided by OATDA will be the ability to fuse data obtained by off-board systems (e.g., Route Surveys, the AN/WLD-1 Remote Minehunting System (RMS), Long-Term Mine Reconnaissance System (LMRS), dedicated MCM systems, other ship's Kingfisher systems) with ownship data in real-time. In addition, we will utilize the contact data fusion and environmental data fusion algorithms developed in our Commander's Estimate of the Situation Tactical Decision Aid (CESTDA) and Environmental Data Fusion for Mine Countermeasures (EDFMCM) projects for ONR to determine a recommended route for the ship that minimizes ship vulnerability to undetected mines or improvised explosive devices (IEDs). As shown in our Cooperative Organic Mine Defense (COMID) work, the ability to utilize non-ownship data significantly improves the ability of the ship to maneuver around potentially threatening objects and dramatically reduces the number of false alarms. The primary algorithmic techniques that will be utilized in OATDA are non-Gaussian and multiple hypothesis data registration and fusion, non-Gaussian optimization, and Bayesian inferential reasoning.

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

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