  Radar pulse streams exhibit increasingly complex temporal patterns and can no
longer rely on a purely value-based analysis of the pulse attributes for the
purpose of emitter classification. In this paper, we employ Recurrent Neural
Networks (RNNs) to efficiently model and exploit the temporal dependencies
present inside pulse streams. With the purpose of enhancing the network
prediction capability, we introduce two novel techniques: a per-sequence
normalization, able to mine the useful temporal patterns; and
attribute-specific RNN processing, capable of processing the extracted
information effectively. The new techniques are evaluated with an ablation
study and the proposed solution is compared to previous Deep Learning (DL)
approaches. Finally, a comparative study on the robustness of the same
approaches is conducted and its results are presented.
