SBIR/STTR Research Award

Modeling and Simulation Technologies to Support Physics Based Active Electronically Scanned Array (AESA) Radar Models in Training Systems

Award identifier 65880386b8504ef91e48fd074f3e61095e4b4077cab259dd505cb2d005d067fdAwarding agency Department of Defense · Air Force

Award amount

$99,836

Program and phase

SBIR · Phase I

Award year

2011

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
INFORMATION SYSTEMS LABORATORIES INC
Proposal award date
2011-03-15
Contract end date
Not reported
UEI
FAK5JKUKRXJ1
Solicitation number
2010.3
Topic code
AF103-024

Research abstract

ABSTRACT: Live, virtual, constructive (LVC) threat simulation in electronic warfare (EW) training systems has allowed pilots to train against dense realistic threat arrays. For example, training simulations provide radar warning receiver (RWR) in-flight training against passive threats such as air surveillance and tracking radars. Recent advances in DRFM technology has increased interest in training pilots against active threats such as repeater jamming. These threats require models of a radar and antenna system. Active Electronically Scanned Arrays (AESA) are replacing the mechanically-steered antennas and are becoming the primary radar antenna architecture and thus are the desired antenna technology for use in the LVC simulations. AESA antennas offer a significant advantage over mechanically-steered antennas including fast beam steering angle changes (beam agility), and formation of multiple beams. AESA antennas are subject to advanced electronic attack (EA) threats like digital radio frequency memory (DRFM) jammers, and can participate in performing EA operations. These capabilities make them more challenging to simulate than traditional mechanically scanned antennas. Under this effort ISL will develop a high-fidelity AESA antenna model for use in LVC simulation environments. BENEFIT: The primary commercialization path for the technology developed under the proposed effort is with Department of Defense program offices that work with or require models of advanced AESA radar systems. Other service branches that have aircraft (both fixed-wing and rotary-wing) training systems that include radars would benefit from the high-fidelity airborne AESA radar modeling and simulation system developed under the proposed effort. The high-fidelity AESA modeling and radar simulation tools developed under the effort are applicable to systems in addition to the airborne AESA radar systems including both ship and ground-based radars.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

155 SBIR/STTR records share this UEI (1986–2025). Reported award amounts total $67,503,247. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Science Foundation, Department of Homeland Security, National Aeronautics and Space Administration, Department of Energy, Department of Education

Agency intelligence

Department of Defense

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.