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A Method for Pedestrian Trajectory Prediction Using INS-GNSS Wearable Devices.

Shengli Pang1, Zhe Wang1, Shiji Xu1

  • 1College of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study introduces a novel multi-source perception fusion system using wearable INS-GNSS devices for enhanced pedestrian trajectory prediction. The system significantly improves localization and prediction accuracy, outperforming existing models.

Keywords:
INS-GNSS integrationadaptive Kalman filterpedestrian trajectory predictionsensor fusionwearable sensors

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Area of Science:

  • Robotics and Artificial Intelligence
  • Geospatial Navigation and Tracking

Background:

  • Pedestrian trajectory prediction faces challenges due to spatiotemporal uncertainty, limiting the accuracy of current machine learning models.
  • Advancements in AI are driving a shift towards neural network-based autonomous decision-making frameworks for trajectory prediction.

Purpose of the Study:

  • To develop a robust multi-source perception fusion system for accurate pedestrian trajectory prediction.
  • To enhance pedestrian localization and future movement forecasting by integrating inertial measurement units (IMUs) and global navigation satellite systems (GNSS).

Main Methods:

  • Proposed a Gait Adaptive UKF (Gait-AUKF) for improved localization by fusing IMU and GNSS data, adapting to pedestrian gait patterns.
  • Developed a multi-source fusion attention mechanism framework using GRU and LSTM for trajectory prediction, incorporating an A* path planning algorithm.
  • Utilized wearable INS-GNSS devices for high-precision data acquisition.

Main Results:

  • The Gait-AUKF significantly reduced localization errors: eastward (30%), northward (26.27%), and vertical (49.08%) compared to UKF and AKF.
  • The complete prediction framework achieved substantial reductions in prediction errors: Average Position Error (APE) by 68.54% and Direction Error (DE) by 70.42% versus LSTM and Transformer models.
  • Ablation studies confirmed that Gait-AUKF and A* path planning improved model performance, decreasing Average Displacement Error (ADE) by 68.49% and Final Displacement Error (FDE) by 71.86%.

Conclusions:

  • The proposed INS-GNSS wearable system and fusion framework offer a significant advancement in pedestrian trajectory prediction accuracy.
  • The Gait-AUKF and attention-based prediction model effectively address spatiotemporal uncertainties in pedestrian movement.
  • This approach holds promise for applications requiring precise pedestrian tracking and forecasting, such as autonomous driving and intelligent surveillance.