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Dense Urban Environment Technologies (DUET)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W900KK-17-C-0034
Agency Tracking Number: A2-6634
Amount: $998,438.52
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A16-075
Solicitation Number: 2016.1
Timeline
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-07-11
Award End Date (Contract End Date): 2019-07-10
Small Business Information
3504 Lake Lynda Drive, Suite 170
Orlando, FL 32817
United States
DUNS: 175966675
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Freddie Santiago
 Principle Investigator
 (407) 601-7847
 fsantiago@dignitastechnologies.com
Business Contact
 Elizabeth Burch
Phone: (407) 601-7847
Email: eburch@dignitastechnologies.com
Research Institution
N/A
Abstract

The current state of Modeling and Simulation (M&S) leaves the bulk of megacity concepts severely under-represented, spanning from lack of source data formats and source data content, to run-time representations and associated models. Dignitas Technologies and teammates Edgewise and Cognitics will leverage extensive expertise in synthetic natural environments to provide a detailed and comprehensive representation of dense urban environments, such as megacities, in modeling and simulation applications. Team Dignitas proposes to conduct analysis, design, development, and experimentation to address a variety of challenges to M&S in dense urban environments.We will focus on developing and strengthening runtime capabilities because we assert that source data requirements should be derived from the definition and evolution of runtime capabilities.Our proposed work plan emphasizes and addresses several areas of traditional weakness in urban M&S environments.We propose to work towards technology solutions in several areas in parallel, and tackle the challenging issue of integrating the SNE services together to form the basis of a cohesive M&S megacity environment.Alone, a solution in each of our research areas will do little to enable dense urban environment SNE modeling.Together, they can demonstrate the possibility of a long-term solution applicable to future M&S training capabilities.

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

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