Kavli Affiliate: Yi Zhou
| First 5 Authors: Zhiyuan He, Ke Deng, Jiangchao Gong, Yi Zhou, Desheng Wang
| Summary:
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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