Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming

In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the cla...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 5; s. 1177
Hlavní autoři:	Zhou, Huaji, Bai, Jing, Niu, Linchun, Xu, Jie, Xiao, Zhu, Zheng, Shilian, Jiao, Licheng, Yang, Xiaoniu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.03.2022
Témata:	Algorithms Bias Categories class exemplar selection class incremental learning Classification Classifiers data collection Datasets Deep learning Distillation electromagnetic signal classification Entropy Linear programming multi-objective linear programming Multiple objective analysis Neural networks normalized mutual information Radiation Radio equipment Receivers & amplifiers Remote sensing Signal classification Signal processing Teaching methods
ISSN:	2072-4292, 2072-4292
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Abstract	In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.
AbstractList	In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.
Author	Zheng, Shilian Xu, Jie Jiao, Licheng Yang, Xiaoniu Bai, Jing Zhou, Huaji Xiao, Zhu Niu, Linchun
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SubjectTerms	Algorithms Bias Categories class exemplar selection class incremental learning Classification Classifiers data collection Datasets Deep learning Distillation electromagnetic signal classification Entropy Linear programming multi-objective linear programming Multiple objective analysis Neural networks normalized mutual information Radiation Radio equipment Receivers & amplifiers Remote sensing Signal classification Signal processing Teaching methods
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Title	Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming
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