  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.
