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.
@article{arxiv.2403.01153,
title = {Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI},
author = {Zhiyuan He and Ke Deng and Jiangchao Gong and Yi Zhou and Desheng Wang},
journal= {arXiv preprint arXiv:2403.01153},
year = {2024}
}