Xinping Rao

In recent years, device-free passive localization leveraging Wi-Fi Channel State Information (CSI) has emerged as a prominent technique for indoor positioning, yet the nonlinear interactions and signal superposition among multiple targets, coupled with occlusion and shadowing effects, significantly complicate the localization task, rendering multi-target device-free passive localization a substantial challenge in the field. In this paper, we propose a novel device-free passive multi-target indoor localization approach based on multi-label learning and unsupervised domain adaptation, denoted as MLDA-MultiLoc. It segments the localization area into multiple training point regions, reformulating the multi-target problem as a multi-label classification task. MLDA-MultiLoc employs a fusion representation model that capitalizes on the spatio-temporal redundancy of CSI amplitude and phase, effectively mapping these features into a unified representation domain. This model is optimized to enhance the discriminative power of the fusion fingerprint (HDFF) by maximizing spatial metrics. Acknowledging the nonlinear influence of multiple targets on CSI, MLDA-MultiLoc incorporates a Fusion Generation Network to synthesize multi-target fingerprints from multiple single-target fingerprints, creating virtual samples for multi-target scenarios. This process facilitates the training of a deep learning-based multi-label classifier, leveraging multi-label learning for robust parameter optimization. Furthermore, MLDA-MultiLoc introduces an unsupervised domain adaptation technique that utilizes a meta-learning dual-stream structure. This method effectively bridges the gap between virtual and real fingerprint samples, ensuring accurate multi-target localization in complex, dynamic indoor settings. Extensive experiments have confirmed the superiority of MLDA-MultiLoc over existing state-of-the-art systems, showcasing its effectiveness in real-world indoor environments.

In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the "unconscious collaborative sensing" in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.