Experimental analysis demonstrates improved positioning accuracy in PDR systems using low-cost IMUs, indicating better navigation solutions for pedestrians.
The Pedestrian Dead Reckoning (PDR) positioning system based on Inertial Measurement Units (IMUs) has attracted significant attention due to its independence from external infrastructure. However, conventional step-model-based PDR methods exhibit declining positioning accuracy over extended distances, limiting their practical application. To address these limitations, we propose a high-precision PDR system employing a low-cost IMU, integrated with a heading constraint model to enhance performance. An Extended Kalman Filter (EKF) is applied for heading estimation, augmented by a directional constraint model to improve pedestrian navigation accuracy. The framework further incorporates comprehensive gait phase identification criteria and an adaptive stride length estimation strategy derived from IMU data. Multi-group long-distance pedestrian walking experiments validate the algorithm’s performance, demonstrating superior positioning accuracy (average position error of 0.36 m; start-end position error of 0.91 m).
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Jiang et al. (2025) studied this question.
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