Enhanced Detection Performance of a TMR Sensor-Based Metal Detector With the Wavelet Denoising Algorithm

The magnetic fingerprint characteristics (MFCs) of active magnetic detection are magnetic signals induced by a metal target that is excited by a primary magnetic field. The detection performance of such a technique is affected mainly by the extraction of MFCs from complex magnetic fields. In this work, we propose a wavelet denoising analysis method to effectively suppress magnetic background noise and extract the MFCs of interest of induced signals in an active magnetic detection system. Moreover, the experimental results demonstrated that the processed wavelet denoising analysis method is consistent with the simulated magnetic dipole model, which can suppress environmental noise to less than 5 nT. The results of the simulations and experiments show that wavelet denoising can effectively remove noise and improve active magnetic detection performance.