Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors

Area of Science:

• Physics
• Engineering
• Geophysics

Background:

• Magnetic sensors are crucial for clinical and industrial applications, including localization and geological surveys.
• Understanding their spatial behavior is key for accurate modeling and solving inverse problems.

Purpose of the Study:

• To present a novel characterization approach for magnetic sensors using adaptive system identification.
• To analyze sensor directivity and frequency responses in 1D, 2D, and 3D.

Main Methods:

• Utilized a gradient-based algorithm for estimating impulse and frequency responses.
• Developed a triaxial Helmholtz coil setup to generate a 3D directive field.
• Employed a control algorithm based on the contraction-expansion approach (CEA) for adaptive system identification.

Main Results:

• Achieved faster convergence and smaller system distances between estimations and measurements.
• Demonstrated efficient impulse response estimation with runtimes under 1.5 seconds per direction.
• Validated the proposed method for frequency and directivity characterization.

Conclusions:

• The novel adaptive system identification approach enables feasible and efficient characterization of magnetic sensors.
• The CEA-based control shows advantages for controlled adaptation and improved accuracy.
• This method has significant potential for advancing applications requiring precise magnetic sensor analysis.