A Deep Learning Approach to Pedestrian Dead Reckoning: Accounting for Latent Variables with Deep Belief Networks

Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) enables infrastructure-free indoor positioning and requires heading-drift compensation associated with gyroscope bias. This paper proposes a stationary-window-based Deep Belief Network (DBN) framework that learns a gyroscope bias representation from z-axis angular-velocity and sampling-interval sequences observed during stationary intervals and applies it to heading correction during walking. The learned representation captures the residual angular-velocity offset under stationary conditions and serves as an adaptive correction term for subsequent heading integration. Experiments on short-term, three-lap long-term, and complex indoor paths show that stationary-window-based DBN bias estimation is particularly effective in accumulated-drift regimes, such as long-term repeated walking and complex multi-turn trajectories. The proposed DBN method achieved an average absolute trajectory error (ATE) of 3.9587 m in the long-term experiment and 0.9126 m in the complex path experiment. Stationary detection sensitivity analysis further shows that false-positive stationary decisions have a stronger influence on trajectory consistency than false-negative stationary decisions. These results show that the proposed framework maintains stable waypoint-level trajectory behavior in long-term and complex PDR scenarios where heading drift becomes more pronounced.