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sUAS Munition Teaming for Advanced Precision Strike

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240522P0005
Agency Tracking Number: S21C-001-0004
Amount: $149,940.90
Phase: Phase I
Program: STTR
Solicitation Topic Code: SOCOM21C-001
Solicitation Number: 21.C
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-25
Award End Date (Contract End Date): 2022-07-31
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138-4555
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Todd Jennings
 (617) 491-3474
Business Contact
 Mark Felix
Phone: (617) 491-3474
Research Institution
 University of Dayton Research Institute
 Zhenhua Jiang
1700 Patterson Blvd
Dayton, OH 45469-7641
United States

 (937) 229-2113
 Domestic Nonprofit Research Organization

Precision-guided munitions have demonstrated dramatic effects with minimal collateral damage. New technology developed specifically to deny them accurate guidance information is now feasible, even for non-traditional adversaries. Further, digital communications are flooding the air with signals that interfere with communications many guidance methods rely on. Swarms of small, covert small Uncrewed Aerial Systems (sUAS) have the potential to provide accurate guidance information even in challenging environments with unreliable communications and little or no other guidance information. Charles River Analytics proposes to design and prototype Wide Area Targeting Computation for Heterogeneous Engagement and Reconnaissance Swarms (WATCHERS). WATCHERS is a coordination, detection, and guidance capability to identify and designate strike targets, and accurately guide inbound indirect fire munitions using a covert sUAS swarm with automatic target recognition (ATR) and guidance information correction as needed by trilaterating its communication signals relative to each sUAS in the swarm. WATCHERS will run on each sUAS, coordinating actions in a robust, fault-tolerant, and interference-resistant manner using probabilistic reasoning derived from established mission parameters. sUAS in the swarm will localize both the target in 3D space using machine learning (ML) computer vision (CV) algorithms and the munition itself to provide real time navigation data in a fault-tolerant, interference-resistant manner.

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

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