TY - GEN
T1 - Embedding Bottleneck Gated Recurrent Unit Network for Radar Signal Recognition
AU - Wang, Yannan
AU - Cao, Guitao
AU - Su, Danning
AU - Wang, Hong
AU - Ren, He
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/18
Y1 - 2021/7/18
N2 - Radar signal recognition plays an significant role in civil applications. Corresponding to two types of intentional modulation signal and unintentional fingerprint signal, radar signal recognition has two kinds of tasks - automatic modulation classification and radar emitter identification. In this paper, we propose a Embedding Bottleneck Gated Recurrent Unit (EBGRU) network that can handle these two tasks separately. The EBGRU consists of three main processing steps. Firstly, the normalized signal pulses are trained in pulse embedding network containing several embedding methods: Pulse2Vec, GloVeP and EPMo, during which we regard the radar signal pulses as radar signal-linguistic sequences for the first time. Then, pulses embeddings are added to original pulses and are sampled to form latent representations of pulses through information bottleneck. Finally, the gated recurrent unit network is utilized to predict radar signal labels. Experiment results show that the proposed method has reached 95.33% on simulated modulation signals and 94.67% at real intercepted emitter signals with relatively less network parameters.
AB - Radar signal recognition plays an significant role in civil applications. Corresponding to two types of intentional modulation signal and unintentional fingerprint signal, radar signal recognition has two kinds of tasks - automatic modulation classification and radar emitter identification. In this paper, we propose a Embedding Bottleneck Gated Recurrent Unit (EBGRU) network that can handle these two tasks separately. The EBGRU consists of three main processing steps. Firstly, the normalized signal pulses are trained in pulse embedding network containing several embedding methods: Pulse2Vec, GloVeP and EPMo, during which we regard the radar signal pulses as radar signal-linguistic sequences for the first time. Then, pulses embeddings are added to original pulses and are sampled to form latent representations of pulses through information bottleneck. Finally, the gated recurrent unit network is utilized to predict radar signal labels. Experiment results show that the proposed method has reached 95.33% on simulated modulation signals and 94.67% at real intercepted emitter signals with relatively less network parameters.
KW - gated recurrent unit network
KW - information bottleneck
KW - modulation classification
KW - pulses embedding
KW - radar emitter identification
UR - https://www.scopus.com/pages/publications/85116499384
U2 - 10.1109/IJCNN52387.2021.9533995
DO - 10.1109/IJCNN52387.2021.9533995
M3 - 会议稿件
AN - SCOPUS:85116499384
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021
Y2 - 18 July 2021 through 22 July 2021
ER -