Robust Pedestrian Dead Reckoning via Adaptive Gait Detection and Heading Drift Correction

Accurate pedestrian localization in global navigation satellite system (GNSS)-denied environments remains a critical challenge. This paper proposes an IMU-based fusion method to address the limitations of current pedestrian dead reckoning (PDR) systems in gait detection and heading drift. The method integrates an Adaptive Time Thresholding algorithm based on Estimated Gait Period (ATT-EGP), a dynamic gyroscope error model with zero-bias compensation for drift reduction and a robust heuristic drift elimination (HDE) algorithm for heading correction. The ATT-EGP algorithm dynamically adjusts thresholds based on real-time gait period estimation, demonstrating consistent performance across three participants. Experimental results show gait detection errors in the range of 0.8%-1.4% for running, 0.6%-2.0% for normal walking, and 0.2%-1.2% for fast walking, achieving improvements of 51.8%-98.3% compared to conventional dual-threshold methods. Combined with the robust HDE algorithm, the system significantly reduces endpoint errors in pedestrian trajectories. Experimental results demonstrate average endpoint error reductions of 54.4% in walking mode, 63.3% in running mode, and 93.1% in multi-motion mode compared to traditional peak detection methods.