The traditional single-frequency metal mine detector is limited by the single-frequency component, which is difficult to meet the requirements of high-accuracy detection of multiple types of mine targets. The modulation square wave is used to transmit the multi-frequency signal based on the multi-frequency electromagnetic induction technology. The phase-locked amplifier is used to extract the signal feature of each frequency component. The naive bayesian algorithm is used to classify metal targets according to the extracted feature vectors. This method can effectively improve target classification accuracy compared with single-frequency metal mine detectors.

This paper proposes a target recognition technique based on target relaxation frequency parameters. These parameters are calculated from the frequency response observed by multi-frequency magnetic induction. Firstly, we prepare multifrequency response and class labels of various materials. The least-square optimization method calculates the prepared materials' relaxation frequency characteristic parameter matrix. Secondly, the matrix is used in the linear regression method to calculate the regression model's undetermined coefficients. Then the coefficients help us to obtain the class label of the target. Finally, we compare the target label with the prepared class labels to identify the substance type of the target. The results show that the technique proposed in this paper can accurately identify and classify the substance type of the target, and the multi-frequency magnetic induction technology based on relaxation frequency can be applied in substance identification.