Association of Critical, Infrequent Data for Network-Centric Multiple Frame Association for Distributed Multiple Target Tracking

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,962.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
W9113M-07-C-0007
Award Id:
77484
Agency Tracking Number:
A062-147-2661
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
PO Box 271246, Ft. Collins, CO, 80527
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
956324362
Principal Investigator:
Benjamin Slocumb
Program Manager II
(970) 413-8343
bjslocumb@numerica.us
Business Contact:
Jeff Poore
Vice President / COO
(970) 419-8343
jbpoore@numerica.us
Research Institute:
n/a
Abstract
The ability to detect, assess and resolve ambiguity in the measurement- or feature/attribute-to-track association process is fundamental to the success of correct fusion of information and to the robustness of combat identification algorithms that depend on the correctness of this association process. To address these issues, Numerica proposes a program based on the detection of short term ambiguity and the use of a Bayesian network tracking data base (BNTD) that makes use of infrequent or very late data to resolve the association uncertainty in support of combat identification. Bayesian network methods are also used to store, retrieve, query, and modify the database to support long-term ambiguity assessment and resolution. The overall objective of this proposed Phase I effort is to demonstrate a proof of concept for using critical but infrequent or very late data to resolve track and combat identification ambiguity in decentralized, distributed MHT/MFA tracking environment. Implied in this objective is the subordinate issue of the construction of proper frames of data on which the association ambiguity rests.

* information listed above is at the time of submission.

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