Can I read Radar Emitter Classification with Attribute-specific Recurrent Neural Networks on EtoBox?

Radar Emitter Classification with Attribute-specific Recurrent Neural Networks by Notaro, Paolo; Paschali, Magdalini; Hopke, Carsten; Wittmann, David; Navab, Nassir is a scholarly article available to read on EtoBox.

What is Radar Emitter Classification with Attribute-specific Recurrent Neural Networks about?

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.

Author

Notaro, Paolo; Paschali, Magdalini; Hopke, Carsten; Wittmann, David; Navab, Nassir

Published

2019

Language

EN