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DTM-Aided Adaptive EPF Navigation Application in Railways.

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This summary is machine-generated.

This study introduces a Digital Track Map-aided adaptive filter for Global Navigation Satellite System/Inertial Navigation System integration. The method enhances real-time positioning accuracy in challenging railway environments by adaptively adjusting measurement noise covariance.

Keywords:
adaptive filteringdigital track mapextended Kalman particle filtertrain navigation application

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Area of Science:

  • Geomatics Engineering
  • Navigation Systems
  • Signal Processing

Background:

  • Global Navigation Satellite System (GNSS) measurement noise covariance varies with operating environments and techniques.
  • Inaccurate covariance modeling degrades GNSS/Inertial Navigation System (INS) Particle Filter (PF) performance.
  • Existing adaptive methods struggle with real-time covariance adjustment.

Purpose of the Study:

  • To introduce a Digital Track Map-aided (DTM-aided) adaptive extended Kalman particle filter for GNSS/INS integration.
  • To enable real-time adjustment of GNSS measurement noise covariance.
  • To estimate GNSS down-direction offset using DTM information.

Main Methods:

  • Developed a DTM-aided adaptive extended Kalman particle filter.
  • Integrated GNSS and INS measurements.
  • Estimated GNSS measurement noise covariance and down-direction offset in real-time.
  • Validated the approach in a railway environment.

Main Results:

  • The adaptive approach significantly improves positioning performance compared to non-adaptive methods.
  • Achieved 62.4% and 14.9% improvements in positioning accuracy over Standard Point Positioning (SPP) and Precise Point Positioning (PPP), respectively.
  • Demonstrated robust performance in complex railway environments.

Conclusions:

  • The DTM-aided adaptive filter effectively adjusts GNSS measurement noise covariance in real-time.
  • The method enhances positioning accuracy and reliability for GNSS/INS integration, especially in challenging environments.
  • The approach is adaptable to different GNSS techniques and complex operational settings.