Magnetic anomaly detection is an effective method for detecting ferromagnetic targets. The practical application of target detection by measuring the magnetic gradient has been confirmed in many studies, and its suppression of background noise has been widely recognized. However, the variation of the unidirectional magnetic field gradient is very small in a short distance, and the amplitude of the signal is also affected by the direction of the target’s magnetic moment and the shaking of the sensor platform itself. These make it difficult to extract the signal from complex background interference. In order to solve these problems, this paper is devoted to use the variation of three axial magnetic gradients in the geomagnetic coordinate system caused by the presence of magnetic target (we call it full magnetic gradient) to construct the magnetic anomaly characteristic of the target. A set of orthonormal basis functions is further derived to linearly describe this characteristic, which improves the signal-to-noise ratio (SNR) of the signal from the perspective of signal energy. The present work main contribution is to propose a full magnetic gradient orthonormal basis function (FMG-OBF) method to effectively characterize magnetic anomaly of magnetic targets. Compared with the traditional one-direction magnetic gradient orthonormal basis function method, the SNR of this method is greatly improved, and it is not affected by a change in the target magnetic moment’s direction nor by the shaking of the sensor carrier platform. Therefore, it has great value in the practical application of aeromagnetic detection.

Active magnetic detection is an effective method for detecting metal targets. Its practical application is confirmed in many previous studies, and its advantages over passive detection have been widely recognized. Most importantly, it can detect weak magnetic or nonmagnetic metal targets, unlike traditional magnetic detection. However, the secondary magnetic field generated by the eddy current on the target excited by the active emission primary magnetic field is always weak, and it has a fast attenuation in space. The target signal measured by the sensor is typically several orders of magnitude smaller than the background interference, which makes it challenging to extract the target signal during detection. This study mitigates these challenges by constructing the secondary magnetic field characteristic of the target from the perspective of signal energy. A set of 13 orthonormal basis functions (OBFs) are further derived to linearly describe this characteristic, which improves the signal-to-noise ratio (SNR) of the signal. The main contribution of this study is the proposal of an eddy current magnetic field OBF (ECMF-OBF) method to effectively characterize the secondary magnetic field of magnetic targets in active magnetic detection. The SNR is considerably improved by the proposed method, and this is independent of the target magnetic moment orientation. Our experiment is performed in different environments, including indoors, lake, and sea, to verify the method’s performance. Therefore, it has excellent value in the practical application of active magnetic detection.

Magnetic anomaly detection (MAD) is an effective method to detect the existence and localization of magnetic targets. Magnetic signal processing technology can extract target signals from complex background noise. However, traditional magnetic signal processing methods cannot greatly improve the signal-to-noise ratio (SNR) while also restoring information concerning the target. This is because the main existing method to calculate a target’s magnetic moment requires a pure target signal. Our research regarding the full magnetic gradient orthonormal basis function (FMG-OBF) addresses the problem of low SNRs for the target magnetic anomaly (TMA) signal. However, this method can only detect the presence of the target and cannot obtain the magnetic moment characteristics of the target. Benefiting from the FMG tensor, which contains large amounts of spatial magnetic field information, this paper is devoted to characterizing TMA in different orientations from the signal energy point of view. We analyze the influences of the target magnetic moment’s variation on the energy components of the TMA signal in various orientations, and further propose a target magnetic moment orientation estimation method. Compared with the traditional signal processing method, the proposed method can estimate the magnetic moment orientation of the target while greatly improving the SNR. Therefore, this method has significant application potential for the classification and identification of weak TMA signals in MAD.

Magnetic anomaly detection (MAD) plays a critical role in both geophysical exploration and environmental monitoring. Despite advancements, traditional MAD methods often struggle with low signal-to-noise ratios (SNR) and complex background noise, limiting their effectiveness. This study introduces a novel magnetic anomaly signal Restormer (MASR) approach, significantly outperforming existing signal restoration techniques in the MAD field regarding accuracy and reliability. Leveraging the unconventional computing power of magnetic tunnel junctions (MTJs), we propose an integrated measurement-computation system designed to implement the MASR method efficiently. Our system demonstrates superior detection performance and marks a departure from conventional von Neumann architectures, offering enhanced computational efficiency and energy savings. By integrating nanodevice-enabled artificial intelligence (AI) directly with signal measurement, our approach minimizes reliance on external processing units and data transmission, ensuring rapid, real-time MAD processing. This system is particularly suited to address the challenges of low SNR and harsh working conditions prevalent in geoscience data analysis. Experimental results obtained from real-world oceanic deployments validate the superiority of the MASR method and the integrated system in achieving high-fidelity magnetic anomaly signal restoration under challenging conditions. This research not only advances the field of MAD but also paves the way for future developments in energy-efficient, compact, and high-performance real-time remote sensing systems, laying the groundwork for broader applications of nanotechnology-enabled AI in autonomous and efficient remote sensing systems.

Target detection technology based on magnetic anomalies, due to its wide detection range, high localization accuracy, all weather conditions and other characteristics, plays an important role in geological prospecting, medical monitoring, disaster rescue and other fields. Some nonmagnetic or weakly magnetic metal targets are difficult to observe with passive magnetic anomaly detection (MAD) methods. The active MAD method based on the eddy current magnetic effect can locate nonmagnetic metal targets. However, traditional detection methods based on magnetic dipoles require knowledge of the target magnetic moment in advance and are not suitable for near-field detection. To solve these problems, this paper establishes a detection model based on a nonmagnetic metal shell target and derives the eddy current magnetic field (ECMF) expression of the nonmagnetic metal shell target. An ECMF extraction method that separates the ECMF from the excitation magnetic field and background interference from the perspective of the signal phase is further proposed. Then, the particle swarm optimization (PSO) algorithm is used to locate the nonmagnetic metal target. When the metal target is located within the range of 1.2 m*1.2 m, the localization error is approximately 2.5%. This method is important and valuable in the fields of medical pill tracking, drug dose conversion monitoring, trapped miner locating, indoor autonomous robot navigation, unexploded object detection and other fields.