Geomagnetic matching navigation technology, as an important assisted navigation method, has gradually been wildly used in the navigation field, in which the quality of geomagnetic reference map is of great significance on improving the guidance and positioning accuracy. However, the existing geomagnetic reference map reconstruction methods can hardly satisfy the actual needs. Considering the popularity of deep learning methods in the image super-resolution (SR) field, a geomagnetic reference map SR method using a convolution neural network is proposed. First, rectangular harmonic analysis theory is used to construct the training dataset, which is based on data from the United States Geological Survey and the International Geomagnetic Reference Field. Then, a multi-channel convolutional neural network, which integrates all the three independent components in the geomagnetic reference field, is proposed of reference map SR reconstruction. Experimental results show the effectiveness of our proposed method over other state-of-the-art methods.

Aeromagnetic interference is one of the important factors limiting the application of aeromagnetic data on aircraft platforms. Therefore, magnetic compensation is necessary for aeromagnetic data processing, which is of great significance to improve the accuracy of geomagnetic navigation. In recent years, aeromagnetic compensation methods can be mainly divided into two categories: linear regression methods based on the Tolles-Lawson (T-L) model and data-driven methods based on machine learning. However, the accuracy of linear regression methods is subject to the complexity of the model and the problem of multicollinearity, while data-driven methods require the quantity and quality of aeromagnetic measurement data. To solve this problem, we proposed an aeromagnetic compensation method taking advantage of both the T-L model and neural network. The T-L model parameters are trained through our network, while the attention mechanism is applied in the hidden layer to enhance the feature extraction ability of the model for time series. We validate our method by applying it to an open-access dataset. The Experimental results demonstrate that our method has higher compensation accuracy and generalization performance than the classical algorithms.

The measurement noise of the standard Kalman filter(SKF) and related filtering algorithms are usually required to obey Gaussian distribution. However, it is hard to be satisfied in practice and the reliability of the filtering results is reduced when the measurement noise is not normally distributed. The robust Kalman filter(RKF) has the ability to bound the abnormal observations by weighing the influence of the updated parameters in accordance with the magnitude of discrepancy between the updated parameters and the robust estimates obtained from measurements at each time step. However, there is a limitation that it wastes the useful information of the state model sometimes. In order to solve the problem of magnetic interference compensation under noise uncertainty, this paper addresses a new method based on the robust M estimation to make the uncertain noise suppressed. The double-model adaptive estimation (DMAE) approach is proposed, which consists of a SKF and a RKF. Two filters are selected adaptively by a conditional probability according to the innovation covariance at each time step. More specifically, the DMAE has ability to resist the measurement noise and make full use of effective information at the same time. Compared with the SKF and the RKF method, the DMAE achieves a better performance in terms of compensation accuracy and robustness for parameter estimation. After compensation, errors of components are reduced from 502nT, 613nT, 493nT to 3.9nT, 2.1nT, 23.9nT respectively with the proposed method, the ideal accuracy of total field is achieved as well.

Three-axis magnetometers are widely used in geomagnetic field navigation. However, they are affected by magnetic distortions when installed in vehicles resulting in sensor errors. Magnetic distortions such as an eddy-current field have not been fully considered yet in compensation work. In this paper, an singular value decomposition-based Rauch–Tung–Striebel (R–T–S) fixed-interval smoothing method is proposed to compensate for the distorted field. Compensation parameters, including eddy-current coefficients, are calculated with the magnetic field data generated by a set of 3-D Helmholtz coils. The experiment is carried out to illustrate that the eddy-current field exists when the magnetic flux changes in the body coordinate system. Moreover, the proposed method has excellent performance that greatly suppresses the error to less than 5 nT, which contributes to the accuracy improvement of magnetic field measurement in the dynamic environment.

Purpose: This study aims to find a novel solution to the calibration of three-axis magnetometers to suppress errors of sensors. The nature of the calibration process is parameter estimation and hence the purpose of the paper is to calculate the error parameters and eliminate sensor errors and obtain the true value of the pure magnetic field. Design/methodology/approach: The paper puts forward a calibration method using an alternative iteration looping optimization (AILO) to estimate the parameters. The proposed method divided the parameters to be estimated into two parts: a portion less than one and the other greater than one. Parameters with different orders of magnitude are calculated respectively, which let one part to be a known quantity and the other part is derived by the known quantity; the derived quantity is used to calculate the known quantity again, and looping iteration multiple times until the iteration termination condition is satisfied. Findings: The simulation and experimental results indicate that the calibration accuracy is improved at least by two orders by the proposed method compared to the two-step method and the linear decreasing weight particle swarm optimization (LDW-PSO) algorithm which proves the validity of the proposed method. Practical implications: The proposed method can improve the calibration accuracy of total magnetic field, which provides a reference to the calibration of three-axis magnetometers. Originality/value: A calibration method based on the AILO is proposed in this paper, which is used to improve the calibration accuracy of the three-axis magnetometer.

The electromagnetic interference emanating from on-board electronic (OBE) equipment can substantially degrade the measurement precision of aeromagnetic fields. To address this issue, this paper proposes a novel and systematic compensation method. First, based on the Pearson correlation coefficient (PCC) and the marginal compensation contribution, a stepwise forward electrical feature selection strategy is designed to provide a compact and reliable input set for the compensation model. Subsequently, an adaptive denoising approach is introduced, which employs iterative correlation coefficient comparisons to determine the appropriate wavelet decomposition level. This enables effective noise reduction for various electrical features while maintaining their essential local features during reconstruction. Furthermore, partial least squares (PLS) is introduced for parameter estimation as a robust alternative to conventional ridge regression. By constructing latent variables, PLS effectively alleviates the ill-conditioning of the model, thereby enhancing compensation accuracy and anti-interference capability. Experimental results across various altitudes and flight conditions show that the proposed method outperforms conventional linear models and common deep learning models in terms of robustness and generalization during aircraft maneuvers. The proposed method provides aeromagnetic data support for precision-critical downstream applications, such as geomagnetic navigation and magnetic anomaly detection.