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Complex Environmental Geomagnetic Matching-Assisted Navigation Algorithm Based on Improved Extreme Learning Machine.

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  • 1National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.

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Summary
This summary is machine-generated.

This study introduces a novel geomagnetic matching navigation algorithm for aerial vehicles, enhancing positioning accuracy and robustness in challenging environments. The proposed method significantly outperforms existing models, enabling real-time, precise location tracking.

Keywords:
Extreme Learning MachineNorthern Goshawk Optimization algorithmgeomagnetic matching assisted navigationmagnetic sensor

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Area of Science:

  • Aerospace Engineering
  • Geophysics
  • Artificial Intelligence

Background:

  • Precise positioning of high-speed aerial vehicles is challenging in environments with satellite signal interference.
  • Inertial navigation systems alone are insufficient for accurate navigation under such conditions.

Purpose of the Study:

  • To propose and validate a Northern Goshawk Optimization (NGO) algorithm-enhanced Extreme Learning Machine (ELM) for geomagnetic matching-assisted aerial vehicle navigation.
  • To improve the NGO algorithm for enhanced matching performance and robustness.

Main Methods:

  • Optimizing Extreme Learning Machine (ELM) initial weights and biases using the Northern Goshawk Optimization (NGO) algorithm.
  • Implementing three enhancements to the NGO algorithm, validated with the CEC2005 benchmark function suite.
  • Generating a geomagnetic matching dataset using the IGRF-13 model for comparative analysis of five models (INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, INGO-BP).

Main Results:

  • The proposed INGO-ELM model achieved real-time positioning (0.27 µs) with average absolute errors of approximately 6.38 m (latitude), 6.43 m (longitude), and 0.0137 m (altitude).
  • The INGO-ELM model demonstrated superior performance compared to four other tested models.
  • The model exhibited excellent robustness, maintaining positioning accuracy within the same order of magnitude even with introduced noise in test set inputs.

Conclusions:

  • The geomagnetic matching-assisted navigation algorithm enables real-time, accurate, and stable positioning for aerial vehicles.
  • The algorithm performs reliably even with observational errors from magnetic sensors.
  • The enhanced NGO-ELM approach offers a significant advancement in aerial navigation technology.