Accession Number:

ADA523977

Title:

Using a Kernel Adatron for Object Classification with RCS Data

Descriptive Note:

Corporate Author:

PHYSICAL SCIENCES INC ANDOVER MA

Report Date:

2010-05-28

Pagination or Media Count:

18.0

Abstract:

Rapid identification of object from radar cross section RCS signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value but require significant preprocessing of the signals. In this paper, we describe the use of a support vector machine for object identification from synthesized RCS data. Our best results are from data fusion of X-band and S-band signals, where we obtained 99.4, 95.3, 100 and 95.6 correct identification for cylinders frusta, spheres, and polygons, respectively. We also compare our results with a Bayesian approach and show that the SVM is three orders of magnitude faster, as measured by the number of floating point operations.

Subject Categories:

  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE