  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.
