Enhanced Pedestrian Trajectory Reconstruction Using Bidirectional Extended Kalman Filter and Automatic Refinement

Pedestrian trajectory reconstruction is essential for applications such as human behaviour analysis and smart transportation systems. Existing methods often struggle with data gaps and inaccuracies due to the limitations of location-acquisition technologies like GPS and WiFi. While some algorithms have been developed to estimate trajectories using IMU data, they only provide relative information and lack absolute location context, necessitating additional efforts and technologies to compensate for this limitation. This paper proposes an enhanced pedestrian trajectory reconstruction method that leverages GPS and IMU data to provide accurate location information across outdoor and indoor areas. The method integrates a bi-directional Extended Kalman Filter (EKF) to fuse GPS and IMU data, iterating forward and backward trajectories. An automatic refinement approach is applied to merge the forward and backward trajectories against multiple residuals, resulting in the final pedestrian trajectory. Evaluations demonstrate the method’s effectiveness using all available GPS data and under challenging conditions with only 5% GPS data. When using all GPS data, the method achieves a median pointwise distance of 4.76 meters and a $\mathbf{6 8 \%}$ CDF error of 7.10 meters. With 5% GPS data, the median pointwise distance is 10.95 meters, with a 68% CDF error of 13.82 meters. These results highlight the method’s robustness and potential for practical applications where GPS data may be sparse or unreliable.