Radio Frequency (RF) System Performance and Electromagnetic Interference (EMI) in Dynamic Environments

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$749,968.00
Award Year:
2011
Program:
SBIR
Phase:
Phase II
Contract:
N68335-11-C-0440
Award Id:
n/a
Agency Tracking Number:
N103-202-0007
Solicitation Year:
2010
Solicitation Topic Code:
N103-202
Solicitation Number:
2010.3
Small Business Information
3015 Village Office Place, Champaign, IL, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
782768977
Principal Investigator:
Duane Setterdahl
Senior Engineer
(217) 363-3396
dsetterdahl@delcross.com
Business Contact:
Matthew Miller
President
(217) 363-3396
mcmiller@delcross.com
Research Institution:
Stub




Abstract
For manned and unmanned vehicles in current military operating environments, many adversary and civilian platforms exist that can be considered non-traditional, unconventional, or otherwise unaccounted for in the design of these vehicles. These unconventional and nontraditional aircraft, ship, and boat targets pose unique challenges to radar system designers, analysts, and operators. To aid in the analysis and design of various radar systems, reliable M & S capabilities are needed to predict radar signatures of the previously described targets and their operating conditions. The radar systems of interest for these M & S capabilities operate at frequencies from L-Band up to the K-Bands, with an emphasis on X-Band frequencies since it is utilized for a number of targeting, tracking, and other radar systems of interest. High-frequency CEM methods are suitable for M & S of radar signatures at these frequencies, given the electrically large nature of the targets. A limited number of high-frequency asymptotic software tools exist for predicting radar signatures of targets, but all have significant shortcomings. Delcross Technologies proposes to develop a new software tool called Signa that can be used to predict radar signatures of electrically large air, ground and sea targets while addressing the shortcomings of existing asymptotic signature prediction tools.

* information listed above is at the time of submission.

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