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Utilizing ML Algorithms to Track and Identify UAS Threats

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: 6SVL4-22-P-0011
Agency Tracking Number: S222-002-0017
Amount: $149,461.50
Phase: Phase I
Program: SBIR
Solicitation Topic Code: SOCOM222-002
Solicitation Number: 22.2
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-07-09
Award End Date (Contract End Date): 2023-01-30
Small Business Information
1861 9th Avenue
Sacramento, CA 95818-4111
United States
DUNS: 116925606
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bryan O'Rourke
 (661) 312-3063
Business Contact
 Kristopher Pruitt
Phone: (916) 707-3178
Research Institution

Special Operations Command is responsible for many of our nation's most critical, no-fail missions, yet the rapid rise of Unmanned Aerial Systems (UAS) is forcing rapid adaptation in the ways these forces can detect, track, and characterize these threats. Our vision is to explore the art of the possible, pairing portable commercial LiDAR sensors with Computer Vision and Deep Learning algorithms to facilitate the identification, localization and pursuant tracking of UAS via a portable, dismounted system. Our feasibility design provides a robust framework to evaluate the best machine learning solutions for object detection across a variety of UAS types, the ability to maintain custody of UAS tracks, and a system design comparative analysis for LiDAR and ML solutions that provide range maximization, detection accuracy, integration with radar, and integration with active mitigation measures. Our solution is backed by experts who bring real-world combat experience, software and machine learning expertise from Silicon Valley, and peer-reviewed research specialization. We have extensive experience designing and developing complex deep learning solutions both commercially and for defense.

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

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