Pedestrian and Router Colocalization Framework Using Distributed-IMU-Based VDR and Wi-Fi RTT

Inertial navigation and Wi-Fi are two common approaches for pedestrian localization. However, conventional pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting suffer from limited adaptability to different users and poor robustness to environmental changes, respectively. Recent deep-learning-based methods address pedestrian localization by modeling sequential dependencies in inertial data, but they typically rely on a single inertial measurement unit (IMU), which is insufficient to capture the spatial correlations of human skeletal motion. In parallel, the fine time measurement (FTM) procedure in IEEE 802.11mc enables round-trip time (RTT)-based ranging and localization, yet the coordinates of Wi-Fi routers still require labor-intensive prior surveying, limiting deployment flexibility. This article presents a pedestrian and router colocalization framework that jointly estimates pedestrian trajectories and Wi-Fi router positions. The proposed framework employs multiple body-worn IMUs and a long short-term memory (LSTM) network to learn both spatial and temporal dependencies in human motion, thereby enabling velocity dead reckoning (VDR). The VDR-estimated pedestrian velocity is then fused with Wi-Fi RTT measurements through factor graph optimization (FGO), in which both pedestrian and router coordinates are treated as unknown variables. Experimental results demonstrate that the multi-IMU-based VDR effectively models pedestrian velocity, while Wi-Fi RTT ranging constrains the long-term drift of VDR. The combined VDR/Wi-Fi RTT framework achieves meter-level positioning accuracy in both indoor and outdoor environments, without requiring presurveyed router coordinates, and thus provides a promising solution for pedestrian localization in the Internet of Things (IoT) era.