Ultra-Wideband RF Discrimination Techniques

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$69,972.00
Award Year:
2002
Program:
SBIR
Phase:
Phase I
Contract:
N0017803C1027
Agency Tracking Number:
022-0038
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Technology Service Corp.
11400 West Olympic Blvd., Suite 300, Los Angeles, CA, 90064
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
053885604
Principal Investigator:
Robert Koda
Corporate Senior Staff
(301) 565-2970
bkoda@tscwo.com
Business Contact:
James Polk
V. President, Washington
(301) 565-2970
jpolk@tscwo.com
Research Institution:
n/a
Abstract
"The objective is to demonstrate the feasibility of fusing extracted Ballistic Missile threat feature data from S- and X- Band radars to improve the probability of correctly identifying the lethal object, compared to using data from either an S- or an X-Band radar alone. This effort will make use of high fidelity S- and X-Band RF polarimetric signature data for cones, spheres, and cyclinders which can be used to represent major objects in a TBM complex. Target RCS is measured by the radar using linearlyprocessed waveforms which employ either non-coherent or coherent pulse-to-pulse integration. Typically non-coherent integration is used to extract narrowband RCS and coherent integration is used to extract wideband RCS. Variations in canonical object RFsignature phenomenology at S- and X-Bands will be examined to identify differentiating features. Examples of differentiating signature phenomenology may include cavity interactions, creeping waves, and traveling waves. Phase I will provide the basisdeveloping RF discrimination algorithms during the Phase II effort that will be tested against BM threat representative signature and trajectory data sets. The discrimination algorithms developed in Phase II will be available for potential insertion intoland and sea-based radars to support BMD mission requirements. We anticipate three approaches to commercialization. The first is the incorporation of the algorithms developed in the US M

* information listed above is at the time of submission.

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