Robust GNSS/PDR Fusion Scheme With Probability Constraints

Aiming at the problem of insufficient robustness of factor graph models in multisensor integrated positioning, especially in Global Navigation Satellite Systems (GNSS) and pedestrian dead reckoning (PDR) fusion scenarios, where existing methods suffer from significant positioning accuracy degradation due to abnormal sensor interference, cumulative error propagation, and dynamic environmental noise, where current robustness improvement solutions mostly rely on manual experience or high-precision sensors while lacking systematic optimization frameworks, this article proposes an adaptive fusion scheme based on cross-time-step constraints. By establishing an inertial sensor noise hard-truncation mechanism and a global historical state reverse-correction model, we propose a multistep kinematic constraint framework based on the state correlation, which enhances the consistency between sensor heterogeneous characteristics and the positioning model, suppresses the long-term impact of PDR cumulative errors on positioning, and reduces the interference of abnormal GNSS observations on system states. Simulation and field experiments show that the proposed scheme can effectively fuse low-cost smartphone sensor data in complex environments, including urban canyons and signal-occluded areas, achieving highly robust integrated navigation and providing theoretical support for seamless positioning in dynamic interference scenarios.