An orthonormalized basis function algorithm based on wavelet analysis for Magnetic Anomaly Detection

• Published in: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP BMEI) pp. 1 - 5
• Main Authors: Zheng, Xin, Xu, Qingfeng, Zhou, Mei, Liu, Hongying, Qiu, Song, Sun, Li, Li, Qingli
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: Approximation algorithms; magnetic anomaly detection; Magnetic fields; Magnetometers; Multiresolution analysis; orthonormalized basis function; Signal processing algorithms; Signal to noise ratio; wavelet analysis
• DOI: 10.1109/CISP-BMEI.2017.8302086

This Magnetic Anomaly Detection (MAD) technology is widely used in various fields in recent years. The focus of most researches is how to improve the signal recognition rate and reduce the false alarm rate. This paper presents an improved Orthonormalized Basis Function (OBF) algorithm for MAD in non-Gaussian noise background. In this algorithm, the raw signal is first decomposed by the wavelet. Then several layers of the decomposed signal with target characteristics are picked out as the reconstructed signal. Finally the magnetic target is recognized by the OBF algorithm. The experimental results show that the noise of the signal can be suppressed to a certain extent and the SNR is improved. Compared with the traditional OBF algorithm, the wavelet analysis based OBF algorithm can detect the target signal accurately and reduce the waste of resources caused by the false alarms.