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RBF Neural Network-Aided Robust Adaptive GNSS/INS Integrated Navigation Algorithm in Urban Environments.

Jin Wang1,2, Ruoyi Li1, Rui Tu1

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
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Summary

This study introduces an improved Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) algorithm. The new method enhances positioning accuracy in urban areas, even with GNSS signal loss, by using a Robust Adaptive Kalman Filter (RAKF) and Radial Basis Function (RBF) network.

Keywords:
GNSS position increment predictionGNSS/INSRBF neural networkRobust Adaptive Kalman Filterurban navigation and positioning

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Area of Science:

  • Navigation Systems Engineering
  • Artificial Intelligence in Navigation
  • Signal Processing for Positioning

Background:

  • Precise positioning in urban environments is challenged by Global Navigation Satellite System (GNSS) signal attenuation and interruption.
  • Inertial Navigation Systems (INS) alone suffer accuracy degradation during GNSS outages.
  • Existing integrated navigation systems require improvement for robust performance in complex scenarios.

Purpose of the Study:

  • To develop an advanced Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated navigation algorithm.
  • To enhance positioning accuracy and robustness under degraded GNSS signal conditions, particularly in urban environments.
  • To mitigate the impact of GNSS signal outages on navigation performance.

Main Methods:

  • Developed a hybrid framework combining a Robust Adaptive Kalman Filter (RAKF) with a Radial Basis Function (RBF) neural network.
  • RAKF adaptively adjusts measurement noise covariance based on GNSS data quality indicators (PDOP, satellite count, signal quality).
  • RBF network predicts pseudo-position increments to substitute for missing GNSS measurements during outages.

Main Results:

  • The proposed RBF-aided RAKF (RBF-RAKF) achieved root mean square (RMS) positioning errors of 0.94m (north), 1.02m (east), and 0.21m (down) during GNSS outages.
  • Demonstrated over 90% improvement in positioning accuracy compared to conventional Extended Kalman Filter (EKF), standard RAKF, and RBF-aided Kalman Filter (RBF-KF).
  • Maintained meter-level horizontal and sub-meter vertical accuracy under severe GNSS signal degradation.

Conclusions:

  • The RBF-RAKF algorithm offers stable and high-precision navigation performance in challenging urban environments.
  • The hybrid approach effectively overcomes limitations of GNSS signal outages and degradation.
  • This method significantly improves the reliability of integrated navigation systems for autonomous applications.