###### Abstract
However, it still has drawbacks, such as substantial metal interference, serious low-resistance layer interference, a requirement for more a priori information, a huge and heavy transceiver system, and so on [14]. In the above-mentioned research, the power frequency magnetic field signal is generally discarded as noise. However, it is widely disseminated and does not need the building of a transmitter-receiver system, which is a superb carrier of magnetic anomaly signals. Power frequency magnetic field signals also have difficulties such as high noise and poor signals. However, with the fast development of magnetic sensor material processing and manufacturing technology, a range of high-sensitivity, high-precision magnetometers has been launched [15; 16], boosting the development of power frequency magnetic detecting techniques. Magnetic targets are inevitably subjected to other magnetic fields (geomagnetic field, power frequency magnetic field, sizable electromagnetic leakage or other magnetic fields) in the natural environment, resulting in magnetic anomaly signals that are easily submerged in magnetic noise [17]. Therefore, efficient detection algorithms are always required to maximize the detection probability. In 2009, [PERSON] et al. divided the magnetic anomaly detection algorithms into two categories [18]: the detection algorithms based on targets and the detection algorithms based on noise. The following conditions govern the target-based detection algorithm. The first is that the distance between the sensor and the target is significantly greater than the target's actual size, satisfying the requirement that the magnetic dipole can be utilized to represent the target [19]. The second is to keep the magnetic moment's magnitude and direction constant. The third step is to determine the target's characteristic time. The sensor load platform's motion path is predetermined, and the spatial position of the target is continually measured throughout the motion. The representative algorithms of this method mainly include the Target Analysis Algorithm based on the magnetic anomaly gradient model proposed by [PERSON] in 1982 [20] and the Orthogonal Basis Function Decomposition Magnetic Anomaly Detection Algorithm proposed by [PERSON] [21]. All of the above methods require a large amount of information to be gathered beforehand and have difficulty solving the variable noise problem. When numerous conditions specified by the target-based detection algorithm are not met, another form of magnetic anomaly signal detection method, namely the statistical analysis approach based on magnetometer noise, should be utilized. The minimum entropy detector, high-order cross magnetic anomaly method, stochastic resonance method, and wavelet transform method are examples of common methods. The noise-based method assumes that the anomaly created by the induced magnetic field generated by the ferromagnetic target alters the pattern of the background magnetic field distribution. This approach does not require prior knowledge of the target's CPA distance or motion direction. This simplifies the calculation. Although the minimal entropy filtering approach is straightforward to calculate, it is difficult to recognize data features when the background noise is complex. The High-order Crossing Magnetic Anomaly Method requires no multiplication, but it is incapable of detecting spatial signals because it is seriously being affected by the fixed plane. The Stochastic Resonance Method improves the output signal-to-noise ratio with noise-enhanced abnormal signals and is also greatly affected by input noises. The wavelet transform method can retain as much target information as possible without being affected by prior information. Based on the excellent characteristics of the wavelet transform method, [PERSON] et al. [22] used the wavelet packet to decompose and extract the components of the geomagnetic signal for analysis. [PERSON] et al.