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.

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.

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.

The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system.

The traditional geomagnetic matching navigation method is based on the correlation criteria operations between measurement sequences and a geomagnetic map. However, when the gradient of the geomagnetic field is small, there are multiple similar data in the geomagnetic database to the measurement value, which means the correlation-based matching method fails. Based on the idea of pattern recognition, this paper constructs a two-stage neural network by cascading a probabilistic neural network and a non-fully connected neural network to, respectively, classify geomagnetic vectors and their feature information in two steps: “coarse screening” and “fine screening”. The effectiveness and accuracy of the geomagnetic vector navigation algorithm based on the two-stage neural network are verified through simulation and experiments. In simulation, it is verified that when the geomagnetic average gradient is 5 nT/km, the traditional geomagnetic matching method fails, while the positioning accuracy based on the proposed method is 40.17 m, and the matching success rate also reaches 98.13%. Further, in flight experiments, under an average gradient of 11 nT/km, the positioning error based on the proposed method is 39.01 m, and the matching success rate also reaches 99.42%.