  We consider the problem of classifying radar pulses given raw I/Q waveforms
in the presence of noise and absence of synchronization. We also consider the
problem of classifying multiple superimposed radar pulses. For both, we design
deep neural networks (DNNs) that are robust to synchronization, pulse width,
and SNR. Our designs yield more than 100x reduction in error-rate over the
previous state-of-the-art.
