Data Fusion Handoff

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,776.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
N68335-07-C-0012
Agency Tracking Number:
N062-109-0399
Solicitation Year:
2006
Solicitation Topic Code:
N06-109
Solicitation Number:
2006.2
Small Business Information
ADAPTIVE METHODS, INC
5885 Trinity Parkway, Suite 230, Centreville, VA, 20120
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
135121148
Principal Investigator:
Walter Allensworth
Sr. V.P.
(301) 840-9722
wallensworth@adaptivemethods.com
Business Contact:
Llew Wood
President
(703) 968-6104
lwood@adaptivemethods.com
Research Institution:
n/a
Abstract
Multi-sensor data fusion is a critical technology used to support contact tracking and association for many Navy missions and systems. Traditional approaches focus on fusing contact position and kinematics data from multiple heterogeneous sensors, with overlapping coverage areas, in order to provide more reliable estimates of the contact’s state. However, these systems fail to meet the requirements for emerging Navy missions, such as the National Maritime Domain Awareness (MDA) goal for persistent surveillance of surface vessels. For MDA and other programs it is critical to be able to reliably associate contacts from one sensor coverage region to another, even with multiple and possibly crossing tracks. In this proposal, an approach for multi-sensor multi-target fusion with persistent track identification numbers is presented. Contact association using Sequential Bayesian Iteration and Inference is discussed and proposed for development in the Phase I work. Development of a particle filter approach for fusion of non-overlapping sensor data is also proposed. Integration of these fusion approaches with a method for propagating track numbers from the sensor, platform, group, theater, and world view-point is also presented. A proof of concept non-real-time simulation of the approach is also proposed.

* information listed above is at the time of submission.

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