Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism
As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) ha...
Main Authors: | Bin Wu, Shibo Yuan, Peng Li, Zehuan Jing, Shao Huang, Yaodong Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/21/6350 |
Similar Items
-
Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network
by: Shengli Zhang, et al.
Published: (2021-11-01) -
Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network
by: Shibo Yuan, et al.
Published: (2021-07-01) -
Specific emitter identification based on one-dimensional complex-valued residual networks with an attention mechanism
by: Lingzhi Qu, et al.
Published: (2021-09-01) -
A Knowledge Graph-Driven CNN for Radar Emitter Identification
by: Yingchao Chen, et al.
Published: (2023-06-01) -
Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network
by: Jing Huang, et al.
Published: (2022-12-01)