  We consider the problem of classifying noisy, phase-modulated radar
waveforms. While traditionally this has been accomplished by applying classical
machine-learning algorithms on hand-crafted features, it has recently been
shown that better performance can be attained by training deep neural networks
(DNNs) to classify raw I/Q waveforms. However, existing DNNs assume
time-synchronized waveforms and do not exploit complex-valued signal structure,
and many aspects of the their DNN design and training are suboptimal. We
demonstrate that, with an improved DNN architecture and training procedure, it
is possible to reduce classification error from 18% to 0.14% on asynchronous
waveforms from the SIDLE dataset. Unlike past work, we furthermore demonstrate
that accurate classification of multiple overlapping waveforms is also
possible, by achieving 4.0% error with 4 asynchronous SIDLE waveforms.
