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Defensive Coordinator for Autonomous Countermeasure Systems

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0318
Agency Tracking Number: N193-145-0077
Amount: $139,996.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-145
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-01-28
Award End Date (Contract End Date): 2020-07-27
Small Business Information
950 Tower Lane Suite 1710
Foster City, CA 94404
United States
DUNS: 079640621
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Stanislav Shalunov
 CEO
 (415) 275-1534
 stas@clostra.com
Business Contact
 Gregory Thiele
Phone: (415) 377-8051
Email: gthiele@clostra.com
Research Institution
N/A
Abstract

Current knowledge and experience in connection with potential conflict scenarios involving Unmanned Air Systems (UAS) is limited. There exists little or no data to inform us how to respond even to situations we can imagine (such as those in which potentially vast numbers of hostile UAVs are sent on offensive missions against us), let alone myriad alternative scenarios beyond the scope of human imagination. A deep learning artificial intelligence solution is proposed that will generate sufficient scenario data to allow UAS operators to train for situations that have not yet been encountered. Additionally, the proposed deep learning solution allows easy modifications of UAS geometries making it possible to test scenarios using alternative form factors for any UAS, or discovering design flaws in existing form factors that could hinder certain required rapid maneuvers. The proposed algorithm development would be carried out with an eye to future extensibility into real-world, rigid-body systems.

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

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