Signal Processing of GMI Magnetic Sensor Based on SVM Regression
Zhang Zhenchuan;Duan Xiusheng;Department of Electronic & Optical Engineering,Army Engineering University Shijiazhuang Campus;
In order to overcome the limitations of the nonlinear properties of giant magneto-impedance(GMI) effects of typical amorphous wire materials. A multi-parameter data processing method of GMI magnetic sensor based on support vector machine(SVM) regression is proposed. Using SVM as a recognition tool, the impedance modulus and impedance phase information of sensitive materials are used as magnetic field identification parameter, the measured magnetic field strength value is taken as the output parameter, and then the SVM model is established and the performance is verified. The results show that the method can overcome the influence of the nonlinearity of the sensitive material, and the processing error is within ?0.007 Oe.
【CateGory Index】： TP212