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Open Call for Innovative Defense-Related Dual-Purpose Technologies/Solutions with a Clear Air Force Stakeholder Need

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0284
Agency Tracking Number: FX192-SO1-0424
Amount: $49,989.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF193-CSO1
Solicitation Number: DoD SBIR X19.2
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-12-12
Award End Date (Contract End Date): 2020-12-12
Small Business Information
3834 Vista Azul
San Clemente, CA 92672
United States
DUNS: 131070141
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 James H Wilson
 President and Chief Scientist
 (949) 366-6554
 jwilson@jovesci.com
Business Contact
 Dr. James H. Wilson
Phone: (949) 366-6554
Email: jwilson@jovesci.com
Research Institution
N/A
Abstract

Jove Sciences, Inc. (JOVE) has just completed an Air Force Research Laboratory Phase II SBIR to improve the performance of the MQ-9 Maritime Wide area Surveillance (MWAS) system by having the MQ-9 Lynx SAR, Full Motion Video, and ISAR Video sensor data transmitted from the ground Control Station (GCS) to JOVE’s Advanced Correlator-Navy (ACOR-N) data fusion processor at the Naval Information Warfare Center-Pacific (NIWC-PAC) at TRl-8 after extremely successful results in five Trident Warrior Exercises. Currently, the MQ-9 MWAS performance in the maritime domain is severely degraded by the Lynx SAR producing far too many ambiguous tracks, and total lack of ship or submarine classification and identification required by the warfighter in the MQ-9 Ground Control Station (GCS), especially on cloudy days when the FMV sensor is not useful. An integrated MQ-9/MWAS/ACOR-N system is being proposed for transition to the MQ-9 Program office (Mr. Lawrence Knight, Mr. Kyle Shelton, and others) to improve the current MQ-9/MWAS performance issues. JOVE proposes here to use Spire Global’s (SPIRE’s) commercially successful worldwide weather and AIS (WW&AIS) ship track capability as inputs to a Knowledge Based Inductive Learning (KBIL) Artificial Intelligent Trusted Agent. One KBIL is assigned to each ship in the world to

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

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