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arXiv · 2403.01153

Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI

Zhiyuan He·Ke Deng·Jiangchao Gong·Yi Zhou·Desheng Wang
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Abstract

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.

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Zhiyuan He, Ke Deng, Jiangchao Gong, Yi Zhou, Desheng Wang. 2024-03-02. Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI. https://arxiv.org/abs/2403.01153

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