Towards Doppler-based Long-Duration High-Precision Localization via Distributed WiFi Infrastructure

With the proliferation of WiFi-enabled IoT devices in modern indoor environments, low-cost passive WiFi localization has attracted significant attention. Unlike time-of-flight (ToF) and angle-of-arrival (AoA)-based approaches, which are constrained by limited bandwidth and antenna counts, Doppler-based passive WiFi localization offers higher accuracy and is applicable to a wider range of low-cost commodity devices. However, due to the position dependency of Doppler speed, existing Doppler-based methods rely on an iterative velocity reconstruction process that requires knowledge of the target's initial position and suffers from severe error accumulation. These limitations hinder the practicality of Doppler-based localization in real-world deployments. In this paper, we propose Wi-WeiFuse (WiFi Weighted Fusion), a novel framework that enables Doppler-based, long-duration, and high-precision localization without requiring knowledge of the target's initial position. Instead of relying on velocity reconstruction and treating the position dependency of Doppler speed as a problem to overcome, Wi-WeiFuse leverages this dependency to directly infer position information from multi-link Doppler speeds. To realize Wi-WeiFuse, we propose a spatiotemporal optimization method that ensures precise position estimation despite interference from local optima induced by complex mappings and random noise. To further improve performance, we introduce a weighted multi-link fusion scheme and theoretically derive the optimal weights, ensuring that Wi-WeiFuse is resilient to quality heterogeneity across distributed links. We derive the DSC-Metric as an optimal fusion weight to address the issue of lacking ground-truth measurements required by the theoretically optimal weights. Extensive experiments show that, without requiring knowledge of the target's initial position, Wi-WeiFuse achieves high-precision localization even over trajectories as long as 100 m. When the trajectory length is 15 m, Wi-WeiFuse reduces the localization error to 26.82% of that of the state-of-the-art baseline, and this ratio further decreases to 3.1% in the 100 m long-trajectory setting.