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Updated: Feb 11, 2026

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Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters.

Jin Woo Song1, Chan Gook Park2

  • 1School of Intelligent Mechatronic Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea. jwsong@sejong.ac.kr.

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Summary

This study introduces an enhanced pedestrian dead reckoning (PDR) algorithm using two cascaded Kalman filters (TCKF) and zero velocity updates (ZUPT). The TCKF method significantly improves navigation accuracy, reducing position errors by up to 90%.

Keywords:
INS-EKF-ZUPTcourse angle errorpedestrian dead reckoningtwo cascaded Kalman filters (TCKF)zero velocity update

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Area of Science:

  • Navigation Systems
  • Sensor Fusion
  • Robotics

Background:

  • Pedestrian dead reckoning (PDR) is crucial for indoor navigation but suffers from accumulated errors.
  • Existing methods like zero velocity update (ZUPU) improve accuracy but have limitations.
  • Accurate estimation of course and heading errors is vital for robust PDR.

Purpose of the Study:

  • To propose an enhanced PDR algorithm using two cascaded Kalman filters (TCKF) for improved navigation accuracy.
  • To integrate inertial measurement units (IMU), magnetic sensors, and ZUPT with TCKF for precise error estimation.
  • To reduce position errors in PDR systems through advanced filtering techniques.

Main Methods:

  • A foot-mounted IMU and waist-mounted magnetic sensors were utilized.
  • A two-stage Kalman filter approach (TCKF) was implemented for course angle and navigation error estimation.
  • Zero velocity update (ZUPT) technique was integrated with an inertial navigation system-extended Kalman filter (INS-EKF-ZUPT).

Main Results:

  • The TCKF effectively estimates course angle error using magnetic sensors and position-trace data.
  • The second stage filter leverages course angle error to enhance heading error estimation, improving yaw gyro bias accuracy.
  • Experimental results demonstrate a maximum reduction in position errors of up to 90% compared to conventional ZUPT-based PDR.

Conclusions:

  • The proposed TCKF-based PDR algorithm significantly enhances navigation accuracy.
  • Integrating magnetic sensors at the waistband mitigates magnetic disturbances for reliable course angle estimation.
  • This method offers a more efficient and accurate solution for PDR compared to existing techniques.