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.

Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

Magnetic anomaly detection (MAD) is widely applied in the fields of resource exploration, hidden target detection, and explosive ordnance disposal. Traditional methods, such as orthonormal basis functions (OBFs), are proposed to extract anomaly signals from ambient noises and device noises. Due to the weakness of the signal, the detection probability has always been limited by a low signal-to-noise ratio (SNR). To surmount the limitation, a full connected neural network (FCN) with OBF features is trained to do the detection. Nonetheless, its effect is not reliable enough under a low SNR, and it is sensitive to the orientations. This letter introduces a multifeature fusion-based neural network with three subclassifiers to conduct MAD. The first subclassifier uses the time–frequency feature, the second uses the statistical feature, and the last concentrates on the magnetic moment feature. The outputs of the subclassifiers are analyzed synthetically by weighted voting, and the optimized weights are picked on the basis of individual performance. The real noise is recorded by experiments to test the performance of our network. The result indicates that a multifeature-based neural network shows a higher detection probability than the ordinary FCN by 5% medially. At very low SNR, the multifeature-based neural network can achieve a detection probability 13% higher than FCN. Sensitivity to orientations is also improved by the multifeature-based neural network.

Potential-field downward continuation is a crucial tool to process gravity and magnetic data, which is capable of effectively enhancing weak anomalies and identifying overlapped ones. However, available methods in this procedure do not include analysis about the impact from different frequencies of the observed magnetic data on the continuation effect. Besides, these methods contain quite a few iteration processes that fix the filter operator and result in their poor performance in reality. In this article, a new downward continuation method is presented, which is based on adaptive filtering within the iteration framework. It finds out the wavenumber distribution of observed data by analyzing its power spectrum, followed by adaptive adjustments on the passband of the filter operator, so as to effectively enhance the convergence speed and continuation accuracy. The simulation results indicated that this method could achieve adaptive adjustments as desired, and it could produce results with higher accuracy and faster convergence rate than the Landweber iterative method can. In addition, tests with actual data revealed a fast and stable downward continuation effect with the proposed method.