Aerial geomagnetic measurement has high strategic significance and application value in geological exploration, unexploded ordnance (UXO) detection, geomagnetic navigation, etc. The magnetic interference of aircraft carrier structure materials and electronic equipment inside the cabin seriously reduces the accuracy of geomagnetic survey. The key to carrier magnetic interference compensation is to realize high-precision estimation of the interference model parameters. However, the estimation accuracy has been limited by the strong interference noises which are not described in the interference model. At the same time, the carrier magnetic interference is dynamically changing. To solve the above problems, a real-time carrier magnetic interference compensation method based on constrained Rayleigh quotient recursive total least squares (RTLSs) is proposed in this article. By considering the input and output errors in the carrier magnetic interference, we established an enhanced interference model, and designed a constrained function on basis of Rayleigh quotient (c-RQ) to acquire an unbiased adaptive solution of the compensation parameters estimation. Using this method, the parameters of carrier magnetic interference compensation model can be calculated in real time based on the prior compensation model parameters obtained by maneuvering calibration flight and the currently updated acquisition data during mission flight. To evaluate the performance of this method, simulation and experimental verification were carried out. Simulation and experimental results show that this method can effectively realize high-precision compensation of carrier magnetic interference compared with traditional methods and machine learning methods. In addition, RTLS has more efficient computing power than traditional methods and machine learning methods.

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.

The decomposition of magnetic dipole signal in orthonormal basis functions (OBFs) is widely used in underwater target detection. Traditionally, only the squared sum of three coefficients is constructed as a magnetic anomaly detector, but the space distribution characteristic of coefficients is not fully utilized. In this letter, the constraint of coefficients is analyzed, and their space distribution constraint that located on an ellipsoid surface is obtained. Moreover, when the magnetization direction of target is nearly horizontal position, the constraint relationship of coefficients in the signal space can be compressed into an elliptical plane. Then, we project the decomposed coefficients of the measured signal onto the elliptical plane to further reduce the noise by about 1/3, and the squared sum of renewed coefficients is constructed as a detector for anomaly judgment. The simulation is conducted, compared with the traditional OBF method, and it can achieve an incremental detection probability of about 6% in the case of low signal-to-noise ratio.

At present, most of the geomagnetic navigation methods are based on the single geomagnetic scalar characteristics, and the iterative closest contour point (ICCP) algorithm is the most extensively utilized. But when there are several contour lines with the same scalar value in the matching area, or the scalar feature in this area is not obvious, navigation accuracy will be seriously affected. In this letter, a new geomagnetic navigation method based on vector matching algorithm [vector ICCP (VICCP)] is proposed, combining the searching principle of trusted points sets and tracks with the matching principle of geomagnetic vector correlation restriction. Consequently, navigation results of it will have greater accuracy, more reliable validity, and practicability compared with the traditional ICCP algorithm. The performance of the matching and the correction methods is analyzed by simulation and experiment. In simulation, the position error of VICCP is less than ICCP under the conditions of nonobvious scalar geomagnetic features, which are, respectively, reduced from 1340.0 to 72.8 m, from 1267.7 to 33.3 m, and from 14115.7 to 36.9 m. And the conclusion is also verified in the experiment. In addition, VICCP algorithm is not sensitive to initial position. Thus, the proposed VICCP algorithm can effectively improve the performance of geomagnetic navigation.

Magnetic anomaly detection is an effective approach for detecting the visually obscured ferromagnetic target, and its performance is mainly limited by background geomagnetic noise. In contrast to the traditional detection methods that rely on several a priori assumptions regarding the target or the probability of magnetic noise consisting of external geomagnetic noise and intrinsic sensor noise, we present, in this letter, a new estimator of information entropy for differential signal acquired by a pair of magnetic sensors to detect any changes in the magnetic noise pattern. First, the magnetic noise probability density function (PDF) of differential signal is estimated by using the kernel smoothing method. Then, the minimum entropy detector based on the magnetic noise PDF of differential signal is used to detect the magnetic anomaly target. Finally, according to the probabilities of false alarm, the detection threshold can be obtained to be used for abnormal judgment. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method has better detection performance than that of traditional methods.

The magnetic interferential fields, such as soft-iron and hard-iron interferences, will seriously affect the accuracy of geomagnetic vector measurement system, and thus should be compensated. In this letter, a new compensation method using a 3-D Helmholtz coil is proposed. As a first step, the geomagnetic vector measurement system is exposed to different directions and the magnitudes of magnetic field generated by a 3-D Helmholtz coil to construct the equations of error model, and soft-iron parameters can be estimated by solving linear equations. Then, hard-iron parameters are estimated by changing the fixation direction of the three-axis magnetometer. Finally, all the estimated parameters are used for compensating distortion magnetic fields. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method contributes to the accuracy improvement of geomagnetic vector measurement system.