A brief review of magnetic anomaly detection

The geomagnetic field is the main magnetic field on the surface of the Earth, and its value is generally much larger than that of ferromagnetic objects. The existence of a geomagnetic field makes the ferromagnetic material magnetized, and the magnetized field will make the local total magnetic field abnormal, so it is called an anomalous magnetic field. This unusual magnetic field is a necessary condition for conducting magnetic anomaly detection (MAD). MAD is a widely used passive method for magnetic target detection, and its applications include surface ship target detection, the monitoring of underwater moving targets, land target detection and the identification of seismic activity for metal mining. MAD technology uses a high-sensitivity magnetometer to measure the target magnetic field. The magnetic field data are used to calculate the position, velocity, volume and other parameters of the target to identify and localize the ferromagnetic target. It is of great significance to study MAD data based on geomagnetic background. This paper reviews the MAD methods proposed by researchers in recent years and summarizes them into two categories. One is target based, and the other is noise based. The target-based group of detection methods involves typical magnetic search systems based on the assumption that the magnetometer and the target move relative to each other, which applies to the case where the target motion obeys a specific tracking time mode. The noise-based detection methods are based on statistical analyses of magnetometer noise and are suitable for situations in which assumptions about the mutual motion of the target and the magnetometer cannot be made. The magnetic dipole model is introduced in the second part of the paper, and then an algorithm based on the standard orthogonal basis function (OBF) decomposition is proposed. The algorithm parallels the target to a magnetic dipole and decomposes it into a linear combination of several standard OBFs. Solving for the coefficients of the basis function yields the signal energy function in the basis function space. The results show that the signal-to-noise ratio of the data processed by the OBF algorithm is significantly improved. The OBF can be further optimized; for example, when using a single magnetometer to conduct MAD, the five OBFs can be simplified to three OBFs; to locate the target more accurately when using two magnetometers to form the gradient magnetometer, the five OBFs can be simplified into four OBFs. The OBF algorithm is not very effective in the detection of non-Gaussian white noise, soa model-based auto-regression method with white filtering can be used. In the third part of the paper, four methods based on noise detection are introduced in detail: the minimum entropy filtering method, the high-order crossing MAD method, the stochastic resonance method and wavelet transform. Their respective principles and detection sensitivities are discussed in detail. At the end of the paper, the MAD methods are summarized, their advantages and disadvantages are discussed, and the future development of MAD is proposed.