Great progress has been made in the integration of Unmanned Aerial Vehicle (UAV) magnetic measurement systems, but the interpretation of UAV magnetic data is facing serious challenges. This paper presents a complete workflow for the detection of the subsurface objects, like Unexploded Ordnance (UXO), by the UAV-borne magnetic survey. The elimination of interference field generated by the drone and an improved Euler deconvolution are emphasized. The quality of UAV magnetic data is limited by the UAV interference field. A compensation method based on the signal correlation is proposed to remove the UAV interference field, which lays the foundation for the subsequent interpretation of UAV magnetic data. An improved Euler deconvolution is developed to estimate the location of underground targets automatically, which is the combination of YOLOv3 (You Only Look Once version 3) and Euler deconvolution. YOLOv3 is a deep convolutional neural network (DCNN)-based image and video detector and it is applied in the context of magnetic survey for the first time, replacing the traditional sliding window. The improved algorithm is more satisfactory for the large-scale UAV-borne magnetic survey because of the simpler and faster workflow, compared with the traditional sliding window (SW)-based Euler method. The field test is conducted and the experimental results show that all procedures in the designed routine is reasonable and effective. The UAV interference field is suppressed significantly with root mean square error 0.5391 nT and the improved Euler deconvolution outperforms the SW Euler deconvolution in terms of positioning accuracy and reducing false targets.

The measurement accuracy of the magnetometer system is closely related to the calibration effect. Available magnetometer calibration methods face the limitation on the assumption that the background magnetic field is uniform. The fact is that the experiment magnetic environment is inhomogeneous for the presence of human activities mainly. This problem could be partially addressed by an auxiliary large-scale triaxial Helmholtz coil to compensate the nonuniformity of magnetic field. This paper proposes a novel calibration method based on the tensor invariants in the nonuniform magnetic field without extra device. The inhomogeneity of background field implies a nonzero gradient field; a new correction model in the gradient field has been established. Calibration parameters are estimated to compensate the observed data using the concept of magnetic gradient tensor and the corresponding rotational tensor invariants, combined with the Levenberg Marquardt optimization. And after the calibration operation, the tensor invariants should be close to a constant. Numerical simulations have been designed and carried out, showing that the proposed algorithm had a good compensation performance with fast convergence, robustness in any gradient field. Field experiments with fluxgate magnetometer array were also conducted to verify this calibration procedure. The measured results are consistent with the simulation results. The root-mean-square (RMS) error is reduced from 616.239 to 1.608 nT/m, after calibration. In addition, the machining error of the frame, the time stability of the background field, the scale of gradient field, and robustness of this proposed method have been discussed.

For unexploded O=ordnance (UXO) detection, individual technology cannot achieve the best detection performance. The new detection mode of joint magnetic and electromagnetic method has attracted more and more attention. In this paper, a newly developed joint detection system is introduced, a multi-rotor UAV-based magnetic system (UAVMAG) and a cart-based time-domain electromagnetic detection system (TDEM-Cart) are combined, and the cooperative processing of magnetic field and electromagnetic data is proposed. The result of the joint inversion fuses the feature vector retrieved from the magnetic field data and the feature vector inverted from the electromagnetic data, providing more accurate positioning results and richer information, which is favorable to locate and distinguish the UXO. Two field experiments are conducted, and the results show that when the joint system works in the full-coverage survey mode, both ferromagnetic and non-ferromagnetic metal targets can be detected, avoiding missed detections. In addition, when the joint system works in the cued survey mode, the detection efficiency is improved, the positioning accuracy of joint interpretation is less than 10 cm, and it shows satisfactory performance in the recognition of targets.

Aeromagnetic surveys using UAVs (Unmanned Aerial Vehicle) have attracted extensive interest, and aeromagnetic compensation is indispensable to improve the quality of magnetic field. For the vector magnetic sensors fixed on the drone, existing compensation methods are based on that the magnitude of vector magnetic field is a constant, but in theory, even if the center position of the UAV remains unchanged during rotation, the magnitude of the measured vector magnetic field is time-varying, because the interference field generated by the drone is related to its attitude, resulting in residual error after compensation. This paper creates a novel small signal model (SSM) based on the measured results of the vector magnetometer, compensating the permanent and induced interference magnetic field generated by the drone. A high-pass filter is used to separate the UAV interference field from the outputs of a vector magnetic sensor, and compensation parameters are estimated by the least square method. Meanwhile, compensation flight is the UAV-borne vector magnetometer system flying multiple lines in a small area, no need to keep the center of the system fixed while performing rotation operations in the air. Field experiments have been conducted, and the root mean square error of the compensated magnetic field is less than 1 nT. In addition, the subsequent test flight and detection experiment were carried out, showing that the SSM compensation can improve the quality of magnetic field.

The magnetometer array plays an important role in the detection of ferromagnetic targets, and the calibration of vector magnetometer array is required. Traditional calibration methods mostly are based on that the magnetic environment is uniform and stable, which cannot match the practical magnetic environment, resulting in the degradation of compensation performance. In this letter, we presented the nonuniform calibration method for the vector magnetometer array, which is based on that redundant tensor element $B_{xy}$ equals to $B_{yx}$ and the tensor rotational invariant remains a constant at one point. The measurement results of magnetometer array include some redundant gradient fields derived from the symmetry of the tensor matrix, and the tensor invariant is independent of posture information, which lay the foundation for the novel correction model together. Besides, the proposed compensation model focuses on the gradient field, eliminating the geomagnetic fluctuations over time largely, and all calibration procedure is completed at one point, avoiding the problem with the spatial nonuniformity of the external magnetic field. The simulation results confirm that the proposed calibration method provides an effective and more accurate compensation performance in the inhomogeneous magnetic environment.