You are here

Hybrid Inferencing for Data Fusion and Situation Assessment

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-05-C-0445
Agency Tracking Number: N045-025-0168
Amount: $499,237.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N04-T025
Solicitation Number: N/A
Timeline
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-09-07
Award End Date (Contract End Date): 2007-09-06
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Subrata Das
 Chief Scientist
 (617) 491-3474
 sdas@cra.com
Business Contact
 Paul Gonsavles
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
 HARVARD UNIV.
 Avi Pfeffer
 
Maxwell-Dworkin Lab., 33 Oxford Street, Room 251
Cambridge, MA 02138
United States

 (617) 496-1876
 Nonprofit College or University
Abstract

We have developed a hybrid inference system for data fusion (DF) and situation assessment (SA) for multi-source intelligence analysis, with specific application to Marine Corps operations in urban environments. The approach views an urban environment as a complex dynamic system whose state vector is composed of a large number of both discrete and continuous variables (hence is hybrid) representing properties of tracked entities. Our inference algorithms exploit both causal dependencies among variables in the state vector via its Dynamic Belief Network (DBN) representation, and vector decompositions into weakly interacting subcomponents via factored Particle Filtering (PF). Under Phase I, the DF and SA functions were demonstrated and evaluated for an ambush scenario in Baghdad. For Phase II, we propose to develop a top-down, library-based methodology for systematically generating DBN models for SA. Incorporating richer dynamics into movement models, along with spatio-temporal clustering of units, will enhance the DF functionality for tracking units. The efficiency of PF algorithms will be enhanced via an asynchronous sampling mechanism. Improved tailoring and customization ability will be provided via libraries of domain expert-specified heuristics and plug-and-play inferencing algorithms. Evaluation will be based on well-defined performance metrics applied to a wide range of scenarios in urban environments.

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

US Flag An Official Website of the United States Government