Identifying Agile Waveforms with Neural Networks

With the advent of widespread digital technology, modern radar and communication systems have grown more complex and agile, rendering them difficult to adequately document and identify. The traditional solution of comparing incoming signals to a library of known waveforms is therefore becoming unworkable. The authors present two solutions to the problem of modern radio frequency (RF) waveform identification: a deep neural network and a recurrent neural network using GRUs. Both networks are designed to fuse together an arbitrary number of agile RF pulses and identify the emitter that produced them. Compared to a naïve DNN approach that simply averaged together pulses before classification, our solution adds a pre-projection step, which preserves information about sequential agility, even after averaging across pulses. After being trained against a set of 15 highly ambiguous emitters, the naïve DNN identified 52.2 % of test waveforms, our DNN with projection identified 72.3 %, and our RNN solution identified 84.8%.