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AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240523P0010
Agency Tracking Number: S23B-001-0036
Amount: $209,761.02
Phase: Phase I
Program: STTR
Solicitation Topic Code: SOCOM23B-001
Solicitation Number: 23.B
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-07
Award End Date (Contract End Date): 2024-03-15
Small Business Information
9150 Rumsey Road Suite A-6
Columbia, MD 21045-1111
United States
DUNS: 080063508
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tony Adams
 (218) 791-7377
 sbir@socom.mil
Business Contact
 Aydin Mohtashamian
Phone: (949) 910-9728
Email: amohtashamian@parrylabs.com
Research Institution
 Southern Mississippi
 Dustin Counsell
 
118 College Dr.
Hattiesburg, MS 39406-0001
United States

 (339) 987-9964
 Nonprofit College or University
Abstract

Existing airborne defense systems integrate a wide variety of sensors necessary to provide operators with situational awareness across the visual, thermal, signals, and electromagnetic spectrums. To date, individual sensor systems have been largely stove-piped, as have Artificial Intelligence/Machine Learning (AI/ML) and advanced, Size, Weight, and Power (SWaP)-optimized data processing systems. The resulting challenge has largely precluded the reuse and feasibility of AI/ML solutions across platforms and sensors, resulting in fewer capabilities being deployed across SOCOM and DoD sensors at a higher cost per capability. To fill this gap, SOCOM requires portable, open architecture solution for orchestration of AI/ML at the edge that consists of three primary pillars: 1) an open architecture computing environment to provide computing resources to mange AI/ML from multiple sensors simultaneously; 2) a container-based software architecture to run AI/ML orchestrators and inference models against sensor data; and 3) a scalable AI/ML development and training pipeline and edge orchestration mechanism to transform cross-sensor data into actionable information.

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

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