Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911787901779968 |
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| author | He, Zhiyuan Deng, Ke Gong, Jiangchao Zhou, Yi Wang, Desheng |
| author_facet | He, Zhiyuan Deng, Ke Gong, Jiangchao Zhou, Yi Wang, Desheng |
| contents | Passive indoor localization, integral to smart buildings, emergency response, and indoor navigation, has traditionally been limited by a focus on single-target localization and reliance on multi-packet CSI. We introduce a novel Multi-target loss, notably enhancing multi-person localization. Utilizing this loss function, our instantaneous CSI-ResNet achieves an impressive 99.21% accuracy at 0.6m precision with single-timestamp CSI. A preprocessing algorithm is implemented to counteract WiFi-induced variability, thereby augmenting robustness. Furthermore, we incorporate Nuclear Norm-Based Transfer Pre-Training, ensuring adaptability in diverse environments, which provides a new paradigm for indoor multi-person localization. Additionally, we have developed an extensive dataset, surpassing existing ones in scope and diversity, to underscore the efficacy of our method and facilitate future fingerprint-based localization research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI He, Zhiyuan Deng, Ke Gong, Jiangchao Zhou, Yi Wang, Desheng Signal Processing Passive indoor localization, integral to smart buildings, emergency response, and indoor navigation, has traditionally been limited by a focus on single-target localization and reliance on multi-packet CSI. We introduce a novel Multi-target loss, notably enhancing multi-person localization. Utilizing this loss function, our instantaneous CSI-ResNet achieves an impressive 99.21% accuracy at 0.6m precision with single-timestamp CSI. A preprocessing algorithm is implemented to counteract WiFi-induced variability, thereby augmenting robustness. Furthermore, we incorporate Nuclear Norm-Based Transfer Pre-Training, ensuring adaptability in diverse environments, which provides a new paradigm for indoor multi-person localization. Additionally, we have developed an extensive dataset, surpassing existing ones in scope and diversity, to underscore the efficacy of our method and facilitate future fingerprint-based localization research. |
| title | Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2403.01153 |