Recent advances in drone-based aeromagnetic survey technology face limitations due to existing interference models and calibration methods cannot adequately compensate for the complex additional magnetic interference on in-cabin magnetometers generated by the on-board electronic (OBE) systems, which require the magnetometer to be mounted on a stinger or wing far from the cabin, restricting drone design and size. To solve this, we propose a compensation method for in-cabin magnetometers. Specifically, in the preprocessing stage, we propose a Recursive Empirical Wavelet Transform Reconstruction algorithm (REWT-R) for more precise current denoising. Then, an Iterative Partial Correlation Screening algorithm (IPCS) is introduced to screen current data for model calibration, avoiding multicollinearity issues. Finally, we eliminate the inaccurate bandpass filtering operation in the calibration stage and improve the loss function with a decomposed weighting scheme, enhancing its global optimization and generalization capabilities. Experiments on public datasets validated the effectiveness of the proposed method at each stage. Meanwhile, compared to the latest methods, our proposed in-cabin magnetic interference compensation method reduces the RMSE by 15 times.

In recent years, significant progress has been made in detecting, localizing, and classifying multiple magnetic dipole sources (DLCMMS) based on magnetic gradient tensors. Traditional DLCMMS methods face challenges due to limited resolution and high-dimensional parameter optimization. In response, we propose a novel DLCMMS method within the Cooperative Coordination (CC) framework. Specifically, we introduce a decomposition strategy within the CC framework to partition the original DLCMMS problem into lower-dimensional sub-problems. We perform joint parameter optimization for source number, positions, and magnetic moment parameters in each sub-problem covering a subset of magnetic sources and measurement points. Moreover, this letter enhances the hybrid optimization algorithm, combined with differential evolution and Levenberg-Marquardt, for joint parameter optimization. In simulations, the number recognition accuracy of sources is 99.3% without noise, about three times higher than traditional methods. During a field demonstration, the position estimation error was below 0.053m.

In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT${}_2$ angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT${}_2$ angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.