Year: 2025
Type: preprint
Abstract: With the rapid advancement of electronic information technology, the number and variety of unknown radiation sources have increased significantly. Some of these sources share common characteristics, which offers the potential to effectively address the challenge of identifying unknown radiation sources. However, research on the classification of radiation sources remains relatively limited. This paper proposes a big data analysis method that combines linear discriminant analysis (LDA) with a rough neighborhood set (NRS) for radiation source classification, and its effectiveness is validated on the RadioML 2018 dataset. The results indicate that, under certain constraints, all modulation types can be categorized into four distinct classes, laying a foundation for further research on cognitive interference signal cancellation.
Source: arXiv (Cornell University)
Institution: Beihang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G6553310163
OpenAlex ID: W7084448375
Cites:
Cited by:
Related to: