Ultra-Wideband RF Discrimination Techniques

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: N0017803C1027
Agency Tracking Number: 022-0038
Amount: $69,972.00
Phase: Phase I
Program: SBIR
Awards Year: 2002
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
11400 West Olympic Blvd., Suite 300, Los Angeles, CA, 90064
DUNS: 053885604
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Robert Koda
 Corporate Senior Staff
 (301) 565-2970
Business Contact
 James Polk
Title: V. President, Washington
Phone: (301) 565-2970
Email: jpolk@tscwo.com
Research Institution
"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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