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An Aeromagnetic Compensation Strategy for Large UAVs.

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Summary

This study presents methods for magnetic compensation in large unmanned aerial vehicles (UAVs) for aeromagnetic surveys. The Lasso regularization Newton iteration (LRNM) method significantly improves accuracy compared to traditional techniques.

Keywords:
Lasso regularization Newton iteration (LRNM)aeromagnetic compensationmulticollinearityunmanned aerial vehicles

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Area of Science:

  • Geophysics
  • Aerospace Engineering

Background:

  • Aeromagnetic surveys are crucial for geological exploration and resource assessment.
  • Large unmanned aerial vehicles (UAVs) are increasingly utilized for magnetic detection due to their payload capacity.
  • Magnetic compensation is essential to mitigate interference from the UAV platform and instruments.

Purpose of the Study:

  • To propose effective magnetic compensation methods for large multi-load UAVs.
  • To address challenges posed by platform interference and instrument noise in UAV-based aeromagnetic surveys.
  • To enhance the accuracy of aeromagnetic survey data collected by UAVs.

Main Methods:

  • Utilized the traditional T-L model to overcome limitations of triaxial magnetometers.
  • Integrated data from inertial navigation systems, global positioning systems, and triaxial magnetometers for direction cosine information.
  • Employed Lasso regularization Newton iteration (LRNM) for magnetic compensation.

Main Results:

  • Increased maneuver amplitude reduced multicollinearity but introduced greater magnetic field interference.
  • LRNM demonstrated significant improvements over traditional least squares (LS) and singular value decomposition (SVD) methods.
  • LRNM achieved 34% and 27% improvements over LS and SVD, respectively.

Conclusions:

  • The proposed compensation schemes are effective for large multi-load UAVs.
  • These methods enhance the practical application of large UAVs in aeromagnetic surveys.
  • Improved accuracy in aeromagnetic data measurement is achieved through these advanced compensation techniques.