## 1 Introduction
[PERSON] et al. [24] recognized and localized magnetic dipole target sources using a coupled network model of YOLO and DenseNet. They demonstrated the potent capabilities of artificial intelligence technology in addressing magnetic dipole source recognition and localization challenges. [PERSON] et al. [25] employed a super-resolution deep neural network to calculate super-resolution NSS combined with an initial rough trust region reflection optimization algorithm to estimate target position parameters. In addition to the position information of magnetic dipole sources, estimating the magnetic moment parameters of magnetic dipoles is a critical issue of interest to researchers. This estimation aids in the subsequent identification and classification of magnetic dipoles. [PERSON] et al. [26] utilized improved tilt angles and normalized magnetic source intensity technology to estimate the position of the target source. They then employed magnetic tensor gradient data and optimization models to estimate the magnetic dipole source's magnetic moment parameters. [PERSON] et al. [27] combined magnetic gradient tensor data with normalized magnetic source intensity and the differential evolution (DE) algorithm to fully estimate the number, position, and magnetic moment of magnetic dipole sources. [PERSON] et al. [28] introduced the VMGT2 angle concept, which determines the number of magnetic sources by mapping the L2 norm of the vertical magnetic gradient tensor onto the arctan function. They combined the differential evolution (DE) algorithm with the LM algorithm to optimize magnetic source parameters. [PERSON] et al. [29] proposed a new method for the detection, localization, and classification of multiple magnetic dipole sources (DLCMMS), achieving joint parameter optimization of magnetic source number, position, and magnetic moment parameters under the cooperative co-evolution (CC) framework, with high recognition accuracy under noise-free conditions. These methods are based on magnetic gradient tensor data, and the measurement instruments are relatively complex and costly. Since the data often contains high-frequency noise and calculating the magnetic gradient tensor from scalar data involves taking unstable second-order derivatives, using scalar data to estimate the position and parameters of magnetic dipole sources is also an essential direction for magnetic source parameter estimation. Scalar measurements are often more accurate than vector and gradient tensor measurements [30]. [PERSON] et al. [31] proposed a recursive differential inversion combined with the Gaussian-Newton method, which can directly estimate the parameters of magnetic dipole sources without additional processing in the presence of external noise and sensor attitude changes. [PERSON] et al. [32] achieved parameter estimation of the ellipsoidal model and actual UXO data under the framework of probabilistic inversion using the ellipsoidal dipole model. Despite significant advancements in magnetic dipole source parameter estimation over recent decades, few scholars have examined the accuracy and stability of these estimates under conditions of substantial interference. Improving the accuracy of magnetic dipole source parameter estimation under strong magnetic interference conditions is a vital issue for the practical application of this technology. Magnetic anomaly detection (MAD) technology primarily studies how to achieve target anomaly detection under strong noise conditions, and a variety of detection methods have been developed, including orthogonal basis decomposition, stochastic resonance, entropy filters, higher-order cross methods, and modal decomposition [33; 34; 35; 36; 37; 38]. The most common method is the orthogonal basis function method (OBF) proposed by [PERSON] et al. [33] in 2002, which decomposes the magnetic anomaly data within a sliding window into three sets of orthogonal basis functions. It then uses the square sum of the function coefficients as the detection basis to achieve target anomaly detection.