Purpose: This paper aims to focus on the tracking of a moving magnetic target by using total field magnetometers and to present a tracking method based on the gradient of a magnetic anomaly. In the tracking, it is assumed that the motion of the target is equivalent to a first-order Markov process. And the unit direction vector of the magnetic moment from the gradient of the magnetic anomaly can be obtained. According to the unit direction vector, the inverse problem is turned into an optimization problem to estimate the parameters of the target. The particle swarm optimization algorithm is used to solve this optimization problem. The proposed method is validated by the numerical simulation and real data. The parameters of the target can be calculated rapidly using the proposed method. And the results show that the estimated parameters of the mobile target using the proposed method are very close to the true values. Design/methodology/approach: The authors focus on the tracking of a moving magnetic target by using total field magnetometers and present a tracking method based on the gradient of a magnetic anomaly. Findings: The paper provides an effective method for tracking the magnetic target based on an array with total field sensors. Originality/value: Comparing with a vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. In this paper, a moving magnetic target was tracked by using total field magnetometers and a tracking method presented based on the gradient of a magnetic anomaly.

Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

The position of a magnetic target can be obtained through magnetic anomaly which is measured by a magnetic sensor. Comparing with vector magnetic sensor, the measurement value of the scalar magnetic sensor is almost not influenced by its orientation in measurement coordinate axes. Therefore, scalar magnetic sensors can be easily assembled into an array. Based on analysis of the total scalar magnetic anomaly measured by scalar magnetometer, we present an efficient method for tracking a magnetic target using scalar magnetometer array. In this method, we separate the position information and magnetic moment information of magnetic target by matrix transformation. Then, we can obtain the position of the magnetic target in real time by a scalar magnetometer array and a particle swarm optimization algorithm. In addition, the magnetic moment of the target can be estimated when the target’s position had been calculated. The simulation shows that the position of the target can be calculated accurately and the relative error of the position is <5 %. The calculated magnetic moment of the target is close to the theoretical value. In addition, execution time of each calculation is <1 s. Thus, the position of the magnetic target can be obtained in real-time through this method.

In the method of target localization based on magnetic anomalies, the scheme of vector field localization and experimental research are significant. Because more information of magnetic field can be measured by vector sensors, the position of the target can be directly calculated by the equations. However, the vector magnetic anomaly generated by the target is difficult to measure. And the detection range is small due to the low sensitivity of vector sensors. A method for target localization based on the total geomagnetic field is proposed. Its advantages are that the measurement of total magnetic field is not affected by the orientation of the total field sensors and the detection range is large due to their high sensitivity. In this paper, we focus on the localization using the array with the total field magnetometers. And we design an array structure with the total field magnetometers. Then, we obtain the higher order nonlinear equations for the target localization based on this array. The numerical method is used to solve the equations. Meanwhile, we present a method for eliminating the effect of geomagnetic field variations and uneven spatial distribution. In suburban roads, localization experiments were carried out. And the results showed that the relative error of target localization is less than 5% by using the proposed method.

The orientation of a vector magnetic sensor can affect the measurement accuracy of magnetic anomaly, thereby increasing the localization error of a magnetic target. Compared with vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. Therefore, we present a method for tracking the magnetic target with a static scalar magnetometer array. In this study, the magnitude of the target’s magnetic moment is a key parameter. We isolate it and formulate an optimization problem based on it to estimate the position and magnetic parameters of the target. To calculate the solution of this optimization problem, a dedicated particle swarm optimization (PSO) algorithm is developed. Then, we define a quality index to evaluate the solution calculated by the optimization problem. The proposed method was validated by the simulation and the real data collected when an SUV car was passing by the array on a straight path. The results show that the tracked trajectory is very close to the true trajectory and the quality index can be used as a criterion to allow accepting or rejecting the localization of the target.