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A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments.

Ahmed E Mahdi1, Ahmed Azouz1, Aboelmagd Noureldin2

  • 1Electrical Engineering Branch, Military Technical College (MTC), Cairo 11766, Egypt.

Sensors (Basel, Switzerland)
|March 28, 2024
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Summary
This summary is machine-generated.

Autonomous vehicles need reliable navigation. Integrating a calibrated Reduced Inertial Sensors System (RISS) with Global Navigation Satellite Systems (GNSS) using Adaptive Neuro-Fuzzy Inference System (ANFIS) significantly improves positioning accuracy in urban areas.

Keywords:
ANFISGNSSINSINS/GNSS integrationMEMS-IMURISSautonomous vehicle navigationmachine learning

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Positioning
  • Machine Learning Applications

Background:

  • Global Navigation Satellite Systems (GNSS) are crucial for autonomous vehicle (AV) navigation but suffer reliability issues in urban environments due to signal blockage and multipath interference.
  • Traditional sensor integration methods for AVs often struggle to maintain accuracy under degraded GNSS conditions.
  • Inertial Navigation Systems (INS), particularly Reduced Inertial Sensors Systems (RISS), offer an alternative but require precise calibration to mitigate drift.

Purpose of the Study:

  • To propose and validate a novel sensor fusion approach for enhancing autonomous vehicle navigation accuracy and reliability in challenging urban environments.
  • To introduce an Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based calibration technique for RISS.
  • To evaluate the performance of the ANFIS-calibrated RISS/GNSS integrated system against traditional RISS/GNSS and Radar-based integrated systems.

Main Methods:

  • Development of a navigation system integrating a Global Navigation Satellite System (GNSS) with a calibrated Reduced Inertial Sensors System (RISS).
  • Implementation of a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) for novel RISS calibration.
  • Validation through real-world road trajectory tests and simulated GNSS outages of varying durations (50-150 seconds).

Main Results:

  • The ANFIS-based RISS/GNSS integration demonstrated a significant reduction in 2D position Root Mean Square Error (RMSE) by 43.8% compared to traditional RISS/GNSS.
  • A 28% improvement in 2D position RMSE was observed compared to a frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated system.
  • The system achieved substantial reductions in 2D position maximum errors: 47.5% versus RISS/GNSS and 23.4% versus Rad/RISS/GNSS.

Conclusions:

  • The proposed ANFIS-based RISS/GNSS integration offers superior positioning accuracy and reliability, crucial for safe autonomous vehicle operation in urban settings.
  • The system exhibits long-term stability and is suitable for applications demanding continuous, precise positioning.
  • The ANFIS calibration approach is extendable to other low-cost Inertial Measurement Units (IMUs), presenting a versatile and attractive solution for diverse navigation applications.