Walking Gaits Aided Mobile GNSS for Pedestrian Navigation in Urban Areas

Pedestrian dead reckoning (PDR) and global navigation satellite system are two popular solutions for pedestrian navigation with a smartphone. pedestrian dead reckoning (PDR) estimates the user’s position by analyzing their walking gaits, including step length and heading angle. However, PDR position errors can accumulate over time due to measurement noise. In contrast, GNSS generates position information by processing radio signals. However, these signals can be affected by blockage and interference. GNSS and PDR are often integrated using a Kalman filter (KF) to provide a more reliable solution. While current integration methods rely on position and velocity measurements, pseudo-range measurements for PDR and GNSS integration still need to be explored. To improve the accuracy of pedestrian position estimation in urban areas, we propose a walking-gaits-aided smartphone GNSS approach. This approach involves employing a factor graph optimization (FGO)-based GNSS/ PDR tight integration method. The FGO- GNSS/ PDR tight integration considers the pseudo-range measurements from each satellite, pedestrian position, and step length to optimize the position estimation. We introduce a fuzzy adaptive FGO (A-FGO) to enhance the accuracy further to suppress pseudo-range outliers. We conducted two experiments using a Samsung Galaxy A40 and Huawei Mate 40 Pro smartphones to evaluate the accuracy of the proposed methods. Our experimental results demonstrate that the proposed methods effectively improve the PDR/ GNSS position accuracy.