A Large Language Model-Guided IMU-PDR Method for Detection and Error Correction

In environments where global navigation satellite system (GNSS) signals are unavailable or unreliable, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) offers considerable potential because it operates independently of external infrastructure. However, maintaining stable and accurate positioning across varying conditions remains challenging. To address this issue, this paper proposes a large language model (LLM)-guided IMU-PDR framework for detection and error correction. The proposed method transforms IMU time-series data into a structured representation tailored to PDR tasks and incorporates domain-specific prior knowledge into prompts to guide the LLM in step-count detection, step-length estimation, heading correction, and subsequent trajectory reconstruction. Experimental results demonstrate that the proposed framework improves step-count detection accuracy, achieves competitive step-length estimation performance, effectively suppresses heading drift, and enhances the overall consistency of reconstructed trajectories. These results suggest that, when guided by domain-specific prior knowledge, LLMs have the potential to interpret and reason effectively over IMU signals, providing a new approach to intelligent detection and error correction in IMU-PDR.