The estimating of the Tolles-Lawson model's coefficients plays an important role in aeromagnetic compensation. The directional cosines, which are indispensable for estimating the coefficients, are usually measured by a three-axis strapdown magnetometer in a general aeromagnetic survey system. However, in some cases, the scalar magnetometer may have a much higher accuracy than the three-axis strapdown magnetometer. This imbalance of the measurement accuracy is then introduced into the Tolles-Lawson model and affects the estimation of the coefficients. In this letter, a modified Tolles-Lawson model is introduced to reduce the imbalance through substituting the error model of the three-axis strapdown magnetometer into the calculation of the directional cosines. The characteristics of the modified model are analyzed and the corresponding coefficient-estimating system is developed. Simulation results illustrate that the modified model is more robust to the larger errors of the three-axis strapdown magnetometer than the classical model.

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal‐to‐noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi‐real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.