Techniques for Developing an Automatic Signal Classifier

Picking up where Introduction to Automatic Signal Classification left off, this paper furnishes details on executing the various steps in the development of an automatic signal classifier. Topics covered include: nonlinear operations that reveal signal characteristics; graphical and statistical methods for deriving signal features; graphical and statistical methods for determining the most powerful features within computational constraints; hierarchical versus Bayesian versus neural network decision logic schemes; primary and secondary logical integration of classification decisions; and testing and performance evaluation.