Recognition of Radar Emitters with Agile Waveform Based on Hybrid Deep Neural Network and Attention Mechanism

With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanism-based method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models.

Keywords

Agility waveform, radar emitter, hybrid deep neural network, attention mechanism

Citation

Radioengineering. 2021 vol. 30, č. 4, s. 704-712. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2021/21_04_0704_0712.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution 4.0 International license
http://creativecommons.org/licenses/by/4.0/