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

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...

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Bibliographic Details
Published in2017 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
LanguageEnglish
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
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DOI10.1109/CISP-BMEI.2017.8302086

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Summary: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.
DOI:10.1109/CISP-BMEI.2017.8302086