Signal Feature Extraction of GMI Sensor in Longitudinally Excitated Amorphous Wire and Its Application in Target Detection

Abstract

In order to expand the dynamic range of the GMI sensor in longitudinally excitated amorphous wire and improve its precision, waveforms of the GMI sensor are analyzed on the background of weak magnetic field measurement. Then three features extraction methods are studied in detail. According to the advantages and disadvantages of different methods, an improved method which combines the energy features of the wavelet decomposition and the amplitude features is proposed. First, fit the amplitude change ratio curve respectively with Gaussian function and polynomial function, which not only solves the problem of nonlinearity, but also improves the measurement accuracy. Considering the difference of signals’ in-pulse features at different positions, the ‘db5’ wavelet is introduced to decompose the signals. Then the BP neural network trained by the energy features of the wavelet is used to locate the target’s approximate position, as a result, the problem of multi-value is solved. At last, experiments of target detection in weak magnetic field prove that the method proposed is effective.