Exploring the Impact of Heading Prediction at Different Time Scales on xDR Indoor Positioning

This work focuses on exploring the accuracy of heading estimation in x dead-reckoning (xDR) positioning methods across different time scales to address the limitations of pedestrian dead-reckoning (PDR) which relies solely on step analysis for location updates. To this end, we first give the relationship among position update frequency of PDR, sampling rate and step frequency in formula which shows the instability. And we propose a method utilizing an improved VIT-Encoder to mine the correlation features of Inertial Measurement Unit (IMU) sequences for heading estimation to eliminate the instability of PDR and evaluate their generalization performance. Results indicate that the model achieves accurate heading estimation on both training and test sets at larger time scales, with substantial disparities observed at smaller scales. Notably, the robustness of smaller-scale estimations mirrors or even surpasses that of longer time scales, revealing an intriguing finding. Additionally, the model exhibits high recognition accuracy across different time scales in straight trajectories, whereas shorter time scales demonstrate advantages at bends, offering higher fault tolerance for misestimations. Increasing the positioning update frequency to 10 Hz in xDR positioning methods is deemed feasible, which holds significant implications for enhancing the performance and user experience of indoor positioning systems, thereby promoting the development and application of indoor positioning technology.