Specific multi-emitter identification via multi-label learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908561366319104 |
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| author | Chen, Yuhao He, Boxiang Wang, Shilian Lei, Jing |
| author_facet | Chen, Yuhao He, Boxiang Wang, Shilian Lei, Jing |
| contents | Specific emitter identification leverages hardware-induced impairments to uniquely determine a specific transmitter. However, existing approaches fail to address scenarios where signals from multiple emitters overlap. In this paper, we propose a specific multi-emitter identification (SMEI) method via multi-label learning to determine multiple transmitters. Specifically, the multi-emitter fingerprint extractor is designed to mitigate the mutual interference among overlapping signals. Then, the multi-emitter decision maker is proposed to assign the all emitter identification using the previous extracted fingerprint. Experimental results demonstrate that, compared with baseline approach, the proposed SMEI scheme achieves comparable identification accuracy under various overlapping conditions, while operating at significantly lower complexity. The significance of this paper is to identify multiple emitters from overlapped signal with a low complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Specific multi-emitter identification via multi-label learning Chen, Yuhao He, Boxiang Wang, Shilian Lei, Jing Signal Processing Specific emitter identification leverages hardware-induced impairments to uniquely determine a specific transmitter. However, existing approaches fail to address scenarios where signals from multiple emitters overlap. In this paper, we propose a specific multi-emitter identification (SMEI) method via multi-label learning to determine multiple transmitters. Specifically, the multi-emitter fingerprint extractor is designed to mitigate the mutual interference among overlapping signals. Then, the multi-emitter decision maker is proposed to assign the all emitter identification using the previous extracted fingerprint. Experimental results demonstrate that, compared with baseline approach, the proposed SMEI scheme achieves comparable identification accuracy under various overlapping conditions, while operating at significantly lower complexity. The significance of this paper is to identify multiple emitters from overlapped signal with a low complexity. |
| title | Specific multi-emitter identification via multi-label learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.22396 |