Recognition and classification of nonlinear chaotic signals.
We propose a method of identifying and classifying signals generated by nonlinear, chaotic processes even when there are other signals and noise present. The method compares probability densities constructed from signals with a library of known densities. We note that there are many different invariant densities that can be constructed and that the characteristic functional of these densities factorizes into a product of characteristic functionals. Each member of the product is the characteristic functional of one of the signals present. This factorization provides a means of identifying the presence of a particular signal. Simple examples are given to demonstrate the method.
Wright
Jon
J
Institute for Nonlinear Science, University of California at San Diego, La Jolla, California 92093-0402Northwest Research Associates, Inc., Bellevue, Washington 98009-3027Institute for Nonlinear Science, University of California at San Diego, La Jolla, California 92093-0402Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green St., Urbana, Illinois 61801-3080Northwest Research Associates, Inc., Bellevue, Washington 98009-3027.
Schult
R. L.
RL