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Compressed sensing based tuning algorithm for the sensor of proton precession magnetometers.

Hengli Song1, Huan Liu1, Haobin Dong1

  • 1School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China.

The Review of Scientific Instruments
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A novel compressed sensing algorithm enhances proton precession magnetometer (PPM) tuning. This OMPCS-FIDR method improves sensor performance, enabling PPMs to operate effectively in strong magnetic field gradients.

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Area of Science:

  • Geophysics and Sensor Technology
  • Signal Processing
  • Applied Physics

Background:

  • Proton precession magnetometers (PPMs) are crucial for geophysical surveys.
  • Accurate sensor tuning is essential for PPM performance, especially in challenging environments.
  • Existing tuning algorithms have limitations in strong gradient magnetic fields.

Purpose of the Study:

  • To develop a new compressed sensing-based tuning algorithm for PPMs.
  • To improve the sensor tuning performance and robustness of PPMs.
  • To enable PPM operation in scenarios with strong gradient magnetic fields.

Main Methods:

  • Developed an end-to-end framework called OMPCS-FID resonance (OMPCS-FIDR).
  • Utilized orthogonal matching pursuit compressed sensing (OMPCS) for sensing free induction decay (FID) signal resonance.
  • Elaborated on the working principle and implementation strategy of OMPCS-FIDR.

Main Results:

  • The OMPCS-FIDR algorithm demonstrated retained performance compared to existing methods.
  • The proposed tuning method overcomes drawbacks of peak detection, auto-correction, and secondary tuning algorithms.
  • The new approach accelerates the possibility of PPMs working in strong gradient magnetic fields.

Conclusions:

  • The OMPCS-FIDR algorithm offers a superior solution for PPM sensor tuning.
  • This advancement enhances PPM applicability in environments with strong magnetic field gradients.
  • The developed method represents a significant improvement in magnetometer technology.