Radar Emitter Signals Recognition and Classification with Feedforward Networks

A possible application of neural networks for timely and reliable recognition of radar signal emitters is investigated. In particular, a large data set of intercepted generic radar signal samples is used for investigating and evaluating several neural network topologies, training parameters, input and output coding and machine learning facilitating data transformations. Three case studies are discussed, where in the first two the radar signals are classified in two broad classes – with civil or military application, based on patterns in their pulse train characteristics and in the third one trained to distinguish between several more specific radar functions. Very competitive results of about 82%, 84% and 67% are achieved on the testing data sets.