Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming
نویسندگان
چکیده
In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing classification (ESC) models cannot handle types. this context, emergence class-incremental learning aims to incrementally update model as categories emerge. paper, an framework based on class exemplar selection and multi-objective linear programming classifier (CES-MOLPC) is proposed in order continuously learn classes incremental manner. Specifically, our approach involves adaptive exemplars considering normalized mutual information classifier. The former used maintain capability for previous by selecting key samples, while latter allow adapt quickly categories. Meanwhile, weighted loss function cross-entropy distillation presented fine-tune model. We demonstrate effectiveness CES-MOLPC method through extensive experiments public RML2016.04c data set large-scale real-world ACARS set. results comparative that can achieve significant improvements over state-of-the-art methods.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14051177