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.

Aeromagnetic compensation is important in aeromagnetic detection. Traditionally, a calibration flight would be implemented to estimate the interference model. However, the magnetic interference is not constant during the mission flight, which makes the interference model constructed in the calibration flight inapplicable in the mission. To solve that problem, this paper proposes an evolutionary aeromagnetic compensation method based on Woodbury equation. With this method, the magnetic interference model parameters can be evolved during the mission flight on basis of the previous interference model and the updated acquired data. To evaluate the performance of the evolutionary method, both simulation and the experiment were conducted. The results indicate that the proposed method can effectively reduce the influence of changing magnetic interference. In the experiment, the standard deviation (STD) of measured data before compensation in the last mission flight is 1.2070nT, the traditional compensation can reduce the STD to 0.0863nT, while our evolutionary aeromagnetic compensation method can reduce STD to 0.0450nT. The results show that increasing changes of magnetic interference will make evolutionary aeromagnetic compensation vital for isolating signals created by geologic features from signals created by the aircraft.

Aerial geomagnetic measurement has high strategic significance and application value in geological exploration, UXO (unexploded ordnance) detection, geomagnetic navigation, etc. The magnetic interference of aircraft carrier structure materials and electronic equipment inside the cabin seriously reduces the accuracy of geomagnetic survey. The key to carrier magnetic interference compensation is to realize high-precision estimation of the interference model parameters. However, the estimation accuracy has been limited by the strong interference noises which are not described in the interference model. At the same time, the carrier magnetic interference is dynamically changing. To solve the above problems, a real-time carrier magnetic interference compensation method based on constrained Rayleigh quotient recursive total least squares (RTLS) is proposed in this paper. By considering the input and output errors in the carrier magnetic interference, we established an enhanced interference model, and designed a constrained function on basis of Rayleigh quotient (c-RQ) to acquire an unbiased adaptive solution of the compensation parameters estimation. Using this method, the parameters of carrier magnetic interference compensation model can be calculated in real time based on the prior compensation model parameters obtained by maneuvering calibration flight and the currently updated acquisition data during mission flight. To evaluate the performance of this method, simulation and experimental verification were carried out. Simulation and experimental results show that this method can effectively realize high-precision compensation of carrier magnetic interference compared with traditional methods and machine learning methods. In addition, RTLS has more efficient computing power than traditional methods and machine learning methods.

Aeromagnetic compensation is essential for enhancing the accuracy of aeromagnetic measurements by estimating and eliminating magnetic interference parameters. Traditional methods typically estimate invariant interference parameters, necessitating regular calibration flights for adjustments. In our previous work, we introduced an evolutionary aeromagnetic compensation method that updates magnetic interference parameters to adapt to variant interferences. While this evolutionary approach is effective, it assumes geomagnetic invariance, which is often not the case during regular low-altitude flights where the geomagnetic field is dynamic and complex. To enhance the applicability of the evolutionary compensation method, this paper presents a modified approach that accounts for geomagnetic variations. We first developed a geomagnetic field model with estimable parameters, which we then integrated with a model of magnetic interference parameters. Building on this integrative parameter model, we refined the evolutionary aeromagnetic compensation method to enable simultaneous estimation of both variable interference and geomagnetic parameters synchronously. The efficacy of our proposed method was validated through simulations and real-flight experiments. Results indicate that, in scenarios with dynamic interference and geomagnetic fluctuations, our method reduces magnetic interference from 0.941 nT to 0.195 nT, which is 21% better than the traditional ridge regression method (TraRR) and 16% better than the conventional evolutionary algorithm (TraEvo) from the literature.

Magnetic interferential compensation plays a vital role in geomagnetic vector measurement applications. Traditional compensation accounts for only the permanent interferences, induced field interferences, and eddy-current interferences. However, nonlinear magnetic interferences are found, which also have a great impact on measurement, and it cannot be fully characterized by a linear compensation model. This paper proposes a new compensation method based on a back propagation neural network, which can reduce the influence of the linear model on compensation accuracy due to its good nonlinear mapping capabilities. The high-quality network training requires representative datasets, yet it is a common problem in the engineering field. To provide adequate data, this paper adopts a 3D Helmholtz coil to restore the magnetic signal of a geomagnetic vector measurement system. A 3D Helmholtz coil is more flexible and practical than the geomagnetic vector measurement system itself when generating abundant data under different postures and applications. Simulations and experiments are both conducted to prove the superiority of the proposed method. According to the experiment, the proposed method can reduce the root mean square errors of north, east, and vertical components and the total intensity from 73.25, 68.54, 70.45, and 101.77 nT to 23.35, 23.58, 27.42, and 29.72 nT, respectively, compared with the traditional method.