Title :
Automatic and Adaptive Correction of Diversionary Errors in Tri-Axial Magnetometer Using Neural Networks
Author_Institution :
Key Lab. of Numerical Control of Jiangxi Province, Jiujiang Univ., Jiujiang
Abstract :
A scalar calibration method is presented using artificial neural network to correct the diversionary errors in a tri-axial orthogonal magnetometer. Firstly, The relations are analyzed between the diversionary errors and magnetometer\´s "intrinsic" parameters, such as orthogonality between axes, amplification and bias of each axes. Then the calibration model with nine unknown parameters is established and a special neural network structure is devised, which can adaptively update the nine calibration parameters through the relationship between the outputs of the magnetometer and the magnetic field applied. A calibration experiment is described briefly, and the experimental results show that the proposed calibration method seems to be a very good candidate for fast or field calibration of a tri-axial orthogonal magnetometer.
Keywords :
calibration; computerised instrumentation; magnetic fields; magnetometers; neural nets; artificial neural network; automatic-adaptive correction; calibration parameters; diversionary errors; magnetic fields; scalar calibration method; triaxial orthogonal magnetometer; Artificial neural networks; Calibration; Error correction; Geomagnetism; Magnetic field measurement; Magnetic fields; Magnetometers; Neural networks; Particle measurements; Programmable control; correction; diversionary errors; magnetometer; neural networks;
Conference_Titel :
Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on
Print_ISBN :
978-1-4244-3530-2
Electronic_ISBN :
978-1-4244-3531-9
DOI :
10.1109/KAMW.2008.4810478