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Dynamic 3-D Threat Mapping Using a Sensor Constellation Deployed on Mobile Platforms

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911SR-11-C-0085
Agency Tracking Number: A2-4600
Amount: $749,983.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: A10a-T022
Solicitation Number: 2010.A
Timeline
Solicitation Year: 2010
Award Year: 2011
Award Start Date (Proposal Award Date): 2011-09-15
Award End Date (Contract End Date): 2012-09-30
Small Business Information
20 New England Business Center, Andover, MA, 01810-1077
DUNS: 073800062
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Bogdan Cosofret
 Area Mgr, Information Exploitation
 (978) 689-0003
 cosofret@psicorp.com
Business Contact
 B. Green
Title: President&CEO
Phone: (978) 689-0003
Email: green@psicorp.com
Research Institution
 University of California, Davis
 Patrick Bell
 1850 Research Park Drive
Suite 300
Davis, CA, 95618-6153
 (530) 754-7700
 Nonprofit college or university
Abstract
In this effort, Physical Sciences Inc. will develop and implement algorithms and hardware to perform the fusion of information obtained from multiple LWIR passive hyperspectral sensors to provide the capability to determine the extent, absolute geo-location, and 3-D concentration distribution of chemical threat clouds from mobile platforms. The effort will be conducted in conjunction with Professor Thomas Strohmer of UC Davis. At the end of a successful Phase II STTR program, PSI will demonstrate a TRL 5 capability based on an extensible architecture. The implementation of the capability is driven by current Nuclear, Biological and Chemical Reconnaissance Vehicle operational tactics and CONOPs. The capability will consist of hardware for sensor pointing and attitude information which will be made available for streaming and aggregation as part of the data fusion process for threat characterization. Threat information (mass estimates, COM location estimate, 3-D concentration) will be generated via multi-sensor (2 or more) data processing employing novel Sparse Tomographic Reconstruction (STR) algorithms which achieve>30% increase in threat reconstruction fidelity over standard methods. The STR algorithms are robust to limited number of projections (i.e. provided by only 2 sensors) and unfavorable geometries resulting from on-the-move operation.

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

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