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Interactive Acoustic Simulation in Urban and Complex Environments

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-11-C-0236
Agency Tracking Number: A11A-006-0059
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A11a-T006
Solicitation Number: 2011.A
Solicitation Year: 2011
Award Year: 2011
Award Start Date (Proposal Award Date): 2011-08-29
Award End Date (Contract End Date): N/A
Small Business Information
305 Brookside Drive, Chapel Hill, NC, 27516
DUNS: 968511936
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dinesh Manocha
 (919) 962-1749
Business Contact
 Anish Chandak
Title: Founder
Phone: (919) 360-3095
Research Institution Chapel Hill
 Barbara Entwisle
 OSR, CB #1350
104 Airport Drive, Suite 2200
Chapel Hill, NC, 27599-1350
 (919) 966-3411
 Nonprofit college or university
Outdoor acoustics simulation plays a vital role in several army and defense-related applications, such as minimizing the noise profile of reconnaissance vehicles to avoid counter-detection, optimizing sensors and systems for tactical advantage, and pinpointing the origin of gunshots using their acoustic signatures. Outdoor acoustics prediction technology is needed to efficiently model large, complex, and dynamic urban and battlefield scenes. State-of-the-art outdoor acoustics prediction methods are restricted, slow, or limited to simple environments. Specifically, no existing method can simultaneously model atmospheric acoustical phenomena, surface interactions with terrain and obstacles, and moving sound sources. We propose to develop a novel hybrid acoustics prediction technique, called multi-domain acoustic transfer (MDAT), which combines numerical and geometric acoustics algorithms. It would handle complex urban and outdoor environments, while running on commodity hardware. It would model broad frequency ranges, atmospheric effects, surface interactions, and dynamic environments with moving sound sources and receivers. Phase I would involve developing the algorithm and a prototype implementation, and evaluating its performance on simple scenes. Phase II would involve developing a full implementation capable of handling large-scale dynamic environments, exploiting multi-core CPUs and many-core GPUs. This implementation would be integrable into existing Army and DoD systems in Phase III.

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

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