Research on Indoor Pedestrian Positioning Method Based on Inertial Sensor Placement Pattern Adaptive Recognition and Improved Inverted Pendulum Model

As public indoor activities become increasingly diversified, the demand for accurate indoor positioning has grown significantly. To address the limitations of GPS in indoor environments, this paper proposes a lightweight pedestrian dead reckoning (PDR) method based on inertial sensors embedded in mobile devices. The approach integrates three key components: (1) a device placement pattern recognition algorithm that classifies stationary, stable, and swing modes using acceleration and angular velocity variance; (2) an adaptive step detection algorithm based on alternating peak-valley rules and temporal thresholds, enabling robust step identification across various usage modes; and (3) a placement-aware stride length estimation model derived from an improved inverted pendulum framework, which dynamically adjusts stride estimates based on walking frequency and device mode. Experimental results demonstrate that the proposed method achieves accurate step detection and positioning performance comparable to civilian GPS systems, while requiring no external infrastructure or prior training. This work offers a practical solution for real-time pedestrian tracking in indoor environments using mobile devices.