Radar Emitter Classification with Attribute-specific Recurrent Neural Networks

TL;DR

This paper introduces attribute-specific RNNs with novel normalization techniques to improve radar emitter classification by modeling complex temporal patterns in pulse streams, outperforming previous deep learning methods.

Contribution

The paper proposes a new RNN-based approach with sequence normalization and attribute-specific processing for enhanced radar emitter classification.

Findings

Improved classification accuracy over previous DL methods

Enhanced robustness of the proposed approach

Effective modeling of complex temporal patterns

Abstract

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.