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Virtual Reality for Multi-INT Deep Learning (VR-MDL)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-C-0328
Agency Tracking Number: F19A-010-0135
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF19A-T010
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-29
Award End Date (Contract End Date): 2022-09-29
Small Business Information
12900 Brookprinter Place, Suite 800
Poway, CA 92064-1111
United States
DUNS: 107928806
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sandeep Gogineni
 (636) 259-6168
Business Contact
 Margaret Latchman-Geller
Phone: (858) 373-2717
Research Institution
 Ohio University
 Jundong Liu
1 Ohio University
Athens, OH 45701-2979
United States

 (740) 593-1603
 Nonprofit College or University

A key goal stated in the United States Air Force Science and Technology Strategy for 2030 and Beyond is to “Increase the speed of battlespace understanding and decision-making to act faster than any adversary.” In this project, ISL and Ohio University (ISL-OU) will build on the success of Phase I and deliver a new game-changing capability for reliably performing complex radio frequency, multi-INT functions including ELINT, SIGINT, and MASINT. The proposed solution has low timing latency and requires extremely low power consumption. More importantly, our approach adaptively improves performance over time and can be developed without expensive flight tests. It leverages ISL’s state-of-the-art M&S tools including RFView® and the real-time hardware-in-the-loop companion tool RTEMES. These high-fidelity radio-frequency M&S and HWIL tools were recently highlighted as a DoD AFRL SBIR Success Story for their ability to dramatically reduce the need for expensive flight testing ( ). The proverbial multi-INT “firehose” is well documented. There is simply too much data for any team of human analysts to effectively digest and process in real (actionable) time. Consequently, for many years now, there have been numerous attempts to “automate” multi-sensor exploitation techniques so as to reduce operator(s) workload. Results have been mixed at best. This, of course, is not unexpected as mining massive amounts of data for ephemeral key intelligence nuggets is as much an art as a science. Recent advances have rekindled interest in developing automated multi-INT fusion based on these new emerging techniques. A complete re-work of the multi-INT architecture is warranted to jointly optimize data collection and exploitation. The ISL team has the qualifications, personnel and tools to successfully develop, demonstrate and transition the proposed novel Multi-INT solutions. ISL is a leader in advanced sensor signal processing and possesses a wealth of domain knowledge across a wide range of sensor types including radar (active/passive), ELINT, SIGINT, MASINT, and EO/IR. ISL has existing data that can be used to demonstrate the signal processing and data fusion concepts. ISL’s approach to achieving the project objectives is to leverage decades of advanced RF technology development experience for some of the most challenging missions for the DoD/IC, apply time-tested analytical and systems engineering methodologies, and exploit world-class RF modeling, simulation and hardware-in-the-loop tools that dramatically burndown development risk. ISL’s personnel are internationally recognized leaders in advanced RF technology development with 100’s of peer reviewed publications, numerous books, patents, and successful technology transition.

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

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