Magnetic Anomaly Detection Based on Singular Spectrum Analysis and Orthonormal Basis Functions

Abstract

Magnetic anomaly detection (MAD) is a method to find the ferromagnetic object by recognizing the weak target magnetic signal in the complex background magnetic noise. In the practical detection, background magnetic noise is usually complex colored noise, so the magnetic noise needs firstly to be suppressed before detecting the measurement magnetic data. In this paper, singular spectrum analysis (SSA) method is firstly introduced to decompose the test data to improve the signal-to-noise ratio (SNR) of detection data. In the decomposing procedure, the clustering method is used to classify the singular values and select out the singular values containing the information of target to reconstruct the new signal to be detected. And then the orthogonal basis functions (OBFs) is applied to detect the reconstructed signal considering that the OBFs has strong resistance to white noise. Some simulation experiments were conducted to show that the detection probability of this method in this paper for the target signal submerged in colored noise is improved by more than 25% compared with the traditional OBFs detection algorithm.