# Walking Dynamics, User Variability, and Window Size Effects in FGO-Based Smartphone PDR+GNSS Fusion

## TL;DR

This paper studies how walking speed and motion affect smartphone positioning accuracy using FGO and finds optimal window sizes for better performance.

## Contribution

The study systematically examines how walking dynamics influence FGO window configuration and PDR error mitigation.

## Key findings

- A window size of around 10 poses balances accuracy and computational load better than smaller windows and approaches batch FGO accuracy.
- Increasing the window to 30 poses provides minimal accuracy gains while increasing computation across all motion types.
- FGO and SWFGO reduce PDR-induced outliers more effectively than KF under varying gaits and disturbances.

## Abstract

The performance of smartphone-based pedestrian positioning strongly depends on the GNSS signal quality, the motion dynamics that influence PDR accuracy, and the way both sources of information are fused. While recent studies have shown the benefits of Factor Graph Optimization (FGO) for Pedestrian Dead Reckoning (PDR) Global Navigation Satellite Systems (GNSS) fusion, the interaction between human motion, PDR errors, and FGO window configuration has not been systematically examined. This work investigates how walking dynamics affect the optimal configuration of sliding-window FGO, and to what extent FGO mitigates motion-dependent PDR errors compared with the Kalman Filter (KF). Using data collected from ten pedestrians performing four motion types (slow walking, normal walking, jogging, and running), we analyze: (1) the relationship between walking speed and the FGO window size required to achieve stable positioning accuracy, and (2) the ability of FGO to suppress PDR outliers arising from motion irregularities across different users. The results show that a window size of around 10 poses offers the best overall balance between accuracy and computational load, providing substantial improvement over SWFGO with a 1-pose window and approaching the accuracy of batch FGO at a fraction of its cost. Increasing the window further to 30 poses yields only marginal accuracy gains while increasing computation, and this trend is consistent across all motion types. Additionally, FGO and SWFGO reduce PDR-induced outliers more effectively than KF across all users and motions, demonstrating improved robustness under gait variability and transient disturbances.