Magnetic anomaly detection is an effective approach for detecting the visually obscured ferromagnetic target, and its performance is mainly limited by background geomagnetic noise. In contrast to the traditional detection methods that rely on several a priori assumptions regarding the target or the probability of magnetic noise consisting of external geomagnetic noise and intrinsic sensor noise, we present, in this letter, a new estimator of information entropy for differential signal acquired by a pair of magnetic sensors to detect any changes in the magnetic noise pattern. First, the magnetic noise probability density function (PDF) of differential signal is estimated by using the kernel smoothing method. Then, the minimum entropy detector based on the magnetic noise PDF of differential signal is used to detect the magnetic anomaly target. Finally, according to the probabilities of false alarm, the detection threshold can be obtained to be used for abnormal judgment. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method has better detection performance than that of traditional methods.

Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.

Magnetic interference of the aircraft platform is one of the most primary restrictions for aeromagnetic missions, like magnetic anomaly detection (MAD) and geomagnetic navigation. In recent years, two main paradigms have emerged for aeromagnetic compensation: model-driven methods based on the linear Tolles-Lawson model and data-driven methods utilizing neural networks. However, model-driven methods struggle with nonlinear interference compensation, while data-driven methods exhibit a high dependence on the quality and quantity of available data. To address the limitations of existing paradigms, this paper introduces a novel approach: an interference model-guided neural network for aeromagnetic compensation. This method leverages the strengths of both model-driven and data-driven techniques. The synergy is achieved through the development of a network initialization method based on T-L estimation and the incorporation of an interference model-embedded loss function. With the proposed method, aeromagnetic compensation can consider both linear and nonlinear interferences, and eliminate them even with limited and noisy data without significant accuracy degradation. The proposed method’s efficacy was validated through real flight tests. Results demonstrate that the proposed method outperforms both the model-driven and data-driven methods, with standard deviations reaching 0.0550nT, compared to 0.0812nT and 0.1306nT, respectively.

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

The decomposition of magnetic dipole signal in orthonormal basis functions (OBFs) is widely used in underwater target detection. Traditionally, only the squared sum of three coefficients is constructed as a magnetic anomaly detector, but the space distribution characteristic of coefficients is not fully utilized. In this letter, the constraint of coefficients is analyzed, and their space distribution constraint that located on an ellipsoid surface is obtained. Moreover, when the magnetization direction of target is nearly horizontal position, the constraint relationship of coefficients in the signal space can be compressed into an elliptical plane. Then, we project the decomposed coefficients of the measured signal onto the elliptical plane to further reduce the noise by about 1/3, and the squared sum of renewed coefficients is constructed as a detector for anomaly judgment. The simulation is conducted, compared with the traditional OBF method, and it can achieve an incremental detection probability of about 6% in the case of low signal-to-noise ratio.

The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system.