An Adaptive Multisource Data Fusion Indoor Positioning Method Based on Collaborative Wi-Fi Fingerprinting and PDR Techniques

With the widespread adoption of smart mobile devices, smartphone-based indoor positioning technology has become a significant area of research. This study proposes a collaborative adaptive multisource data fusion method for indoor positioning, utilizing the inherent multisensor capabilities of smartphones. In the offline phase, a multilayer perceptron (MLP) is trained using collected Wi-Fi fingerprint data. Specifically, we recorded 114 fingerprint locations in Scenario 1 and 99 in Scenario 2, each containing 20000 fingerprint datasets. During the online phase, the system collects real-time Wi-Fi fingerprint data and matches it against the trained dataset. Subsequently, the user's step length and direction data are integrated into a pedestrian dead reckoning (PDR) algorithm to estimate positions within complex indoor environments. Finally, we apply an adaptive extended Kalman filter (AEKF), which adjusts weights to further improve positioning accuracy. Experimental results demonstrate that the AEKF method reduces root-mean-square errors (RMSEs) to 0.763 and 0.884 m in scenarios with different complexities, enhancing accuracy by 10.13% and 30.17% compared with the traditional EKF methods. Comparative analysis with mainstream algorithms further highlights the advantages of AEKF, achieving maximum accuracy improvements of up to 36.54%. These findings underscore the effectiveness of our approach in advancing indoor positioning technology for mobile applications, offering practicality and cost-effectiveness without requiring additional hardware.