Magnetic compensation is a necessary step in the aeromagnetic data processing. While the aeromagnetic compensation model is a linear regression model, the multicollinearity of the variables in the model reduces the accuracy of the compensation model. To solve this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE searches the direction of maximum change in the data by using the gradient descent backpropagation algorithm. The special structure of the encoder can compress the representation of the coefficient matrix and extract data features, thereby weakening the correlation between the coefficient matrix variables. The features obtained after dimension reduction are used in the compensation calculation. We validate the DAE algorithm by applying it to data collected by an unmanned aerial vehicle and demonstrate that the DAE magnetic compensation results are better than those of the principal component analysis algorithm.

Aeromagnetic compensation plays an important role in aeromagnetic data processing and can eliminate the magnetic interference generated by aircraft. Currently, aeromagnetic compensation algorithms are mostly based on linear regression equations, but linear regression has insufficient fitting function capabilities compared to neural networks. Williams proposed a compensation model from the perspective of a neural network, but this model has the problem of overfitting. Combining these two methods, this paper proposes a neural network compensation method with strong generalization. This method is based on regression equations and uses generalized regression neural networks (GRNNs) for neural network regression. Analysis of the probability density function that constitutes the GRNN shows that proper smoothing factors and multiple training sets can improve the generalization of the GRNN compensation model, thereby weakening the effect of overfitting.

As neural networks become an increasingly popular technique in the field of aeromagnetic compensation, there is an increasing demand for hardware systems with more computing power. Compared with the linear regression method, applying a neural network to the task of real-time compensation is difficult because of insufficient computing resources in the unmanned aerial vehicle (UAV) flight detection platform. To perform real-time compensation calculations with limited computing resources, we optimized back propagation neural network (OBPNN) through model compression and acceleration. In this study, we found that the most time-consuming part of network training is the iterative updating of the weights in the BPNN interference model. Using transfer learning, we replace the randomly initialized weights (RWs) with pretrained weights, thereby greatly reducing the number of iterations required. We also apply other model compression and acceleration algorithms. In a case study of our new technique, we implement the fast training of the OBPNN on a Raspberry Pi 4B system. This network processes approximately 316 samples per 0.1 s, which is fast enough to complete aeromagnetic compensation in real time.

Aeromagnetic compensation is a crucial step in the processing of aeromagnetic data. The aeromagnetic compensation method based on the linear regression model has poorer fitting capacity than the neural network aeromagnetic compensation algorithm. The existing gradient updating neural network-based aeromagnetic compensation algorithm is subject to the problem that the gradient disappears during the backpropagation process, resulting in poor fitting ability and affecting aeromagnetic compensation accuracy. In this paper, we propose a neural network compensation algorithm with strong fitting ability: residual backpropagation neural network (Res-bp). The algorithm realizes the cross-layer propagation of the gradient through a residual connection so that the network not only preserves the original information but also acquires additional information during training, successfully solving the problem of gradient disappearance and boosting the network’s fitting capacity. The algorithm is applied to the data collected by unmanned aerial vehicles (UAVs) to verify its effectiveness. The results show that the improvement ratio is improved compared with the traditional neural network, demonstrating that the algorithm has a significant compensation effect on aeromagnetic interference and improves the quality of aeromagnetic data.

Magnetic compensation is a necessary step in aeromagnetic data processing. The aeromagnetic compensation model is a linear regression model, but the model has multiple collinearity problems, which will reduce the performance of the compensation model. In view of this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE network extracts the features of the data by learning the compressed representation of the coefficient matrix, thereby weakening the correlation between the coefficient matrix variables. The feature obtained after dimension reduction is used for compensation calculation. The DAE algorithm is verified by Unmanned Aerial Vehicles, and the results show that the compensation quality of the DAE is better than the least squares algorithm.