FTAN: Feature Transform and Alignment Network for cross-domain specific emitter identification

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-11-22 DOI:10.1016/j.sigpro.2024.109800
Zhiling Xiao, Xiang Zhang, Guomin Sun, Huaizong Shao
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Abstract

Conventional deep learning-based specific emitter identification (SEI) methods are consistently constrained to domain-invariant assumption, leading to a decrease in recognition accuracy when the feature domain changes. To tackle this issue, we propose a novel unsupervised domain adaptation (UDA) framework named feature transform and alignment network (FTAN) for cross-domain SEI. In FTAN, we first apply a weight-shared network to extract the initial features of signals from all domains. Then, we introduce domain-specific modules to individually learn domain-invariant features, which can minimize the distribution discrepancies of source and target signals. Finally, the aligned domain-invariant features are utilized for identification. We evaluate the performance of FTAN on the various signal datasets. The experimental results demonstrate that FTAN significantly mitigates identification performance degradation in cross-domain scenarios and outperforms other state-of-the-art methods.
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跨域特定发射极识别的特征变换与对准网络
传统的基于深度学习的特定发射器识别(SEI)方法一直受到域不变假设的约束,导致特征域变化时识别精度下降。为了解决这个问题,我们提出了一种新的无监督域自适应(UDA)框架,称为跨域SEI特征转换和对齐网络(FTAN)。在FTAN中,我们首先应用一个权重共享网络来提取所有域信号的初始特征。然后,我们引入特定领域的模块来单独学习领域不变特征,从而最大限度地减少源信号和目标信号的分布差异。最后,利用对齐后的域不变特征进行识别。我们评估了FTAN在各种信号数据集上的性能。实验结果表明,FTAN显著减轻了跨域场景下的识别性能下降,优于其他最先进的方法。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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