Geomagnetic Matching Navigation Research Articles

Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes-the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

A high-altitude geomagnetic matching area selection approach based on geomagnetic information entropy and geomagnetic direction entropy

Aiming at the obvious trend of the contours on the high-altitude geomagnetic map, this paper presents a method of high-altitude geomagnetic matching area selection combining geomagnetic information entropy and geomagnetic direction entropy based on the study of geomagnetic information entropy and geomagnetic direction entropy. The method first uses geomagnetic information entropy to select the region with rich geomagnetic information, and then determines the direction of the flight trajectory according to geomagnetic direction entropy to obtain the optimal matching trajectory. Finally, the method compares and analyzes the matching localization results of three flight trajectories in different directions on the geomagnetic map at an altitude of 30,000 m by using semi-physical simulation. The experimental results show that the flight navigation error along the trajectory with small information entropy is small, and its positioning error is 12.7% of the trajectory positioning error along the maximum information entropy direction. Selecting the flight trajectory according to the value of the geomagnetic direction entropy can greatly improve the precision and reliability of the geomagnetic matching localization. The method in this paper can provide a basis for path planning of the geomagnetic matching navigation.

A Geomagnetic/Odometry Integrated Localization Method for Differential Robot Using Real-Time Sequential Particle Filter.

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively.

Geomagnetic reference map super-resolution using convolutional neural network

Geomagnetic matching navigation technology, as an important assisted navigation method, has gradually been wildly used in the navigation field, in which the quality of geomagnetic reference map is of great significance on improving the guidance and positioning accuracy. However, the existing geomagnetic reference map reconstruction methods can hardly satisfy the actual needs. Considering the popularity of deep learning methods in the image super-resolution (SR) field, a geomagnetic reference map SR method using a convolution neural network is proposed. First, rectangular harmonic analysis theory is used to construct the training dataset, which is based on data from the United States Geological Survey and the International Geomagnetic Reference Field. Then, a multi-channel convolutional neural network, which integrates all the three independent components in the geomagnetic reference field, is proposed of reference map SR reconstruction. Experimental results show the effectiveness of our proposed method over other state-of-the-art methods.

Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation.

A New Geomagnetic Vector Navigation Method Based on a Two-Stage Neural Network

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%.

Geomagnetic/inertial navigation integrated matching navigation method

The accuracy of an a priori geomagnetic reference map is a significant constraint on the geomagnetic matching navigation method. Especially when the aircraft enters an unknown environment, it is impossible to accomplish the navigation task using traditional geomagnetic navigation methods. Therefore, a geomagnetic/INS integrated matching navigation algorithm for long-scale navigation is proposed in this paper, which can perform navigation tasks regardless of whether there is an a priori geomagnetic map. This method combines a magnetogram-based matching navigation method with a geomagnetic bionic navigation method that does not require a geomagnetic map. The navigation trajectory is first analyzed in the area where the geomagnetic map is available, and the matching results are further corrected by filters for the inertial navigation system. Furthermore, when the aircraft flies through an unknown area without the a priori geomagnetic map, the nonmagnetic map navigation method is used to guide the aircraft to the destination. Finally, simulations are performed for the geomagnetic map distribution and noise effects that may occur during actual flight, and the obtained results show the effectiveness of the proposed algorithm in a complicated geomagnetic environment.

Summary of Research on Geomagnetic Navigation Technology

Summarizes the technical principles of geomagnetic matching navigation, expounds the navigation solution process of MAGCOM, SITAN and ICCP three main matching navigation algorithms, and discusses the application status of geomagnetic navigation technology from the three geographical environments of satellite, air and underwater. It points out the research prospects of geomagnetic matching navigation and provides reference for the research and development of geomagnetic navigation technology.

A Magnetic Potential-field Downward Continuation Method Based on Accelerated Landweber Iteration

A high-precision geomagnetic database is the basis of geomagnetic matching navigation. In the construction of a geomagnetic database, it is necessary to use magnetic field extension technology to construct the vertical height relationship of geomagnetic data, in which downward continuation is mathematically ill-posed, and Landweber iterative method is a common solution. However, the convergence rate of the Landweber iterative method for the “non-smooth“ solution is very slow, resulting in low delay accuracy in dealing with local magnetic anomalies. Therefore, to solve the problem of slow convergence of the Landweber iterative method, a downward continuation method based on accelerated Landweber iteration is proposed in this paper. In the framework of the iterative method, stable upward continuation is used to connect the observation surface with the magnetic field values on the plane with the same height as upward continuation and downward continuation. Then the residual term is constructed by the vertical first-order derivative relation, and then the residual term is modified by using the low-pass filtering characteristic of the upward continuation operator to suppress the high-frequency noise in the residual term and finally update the iterative value. The simulation results of magnetic field models with different field source depths show that the improved method has faster convergence speed and higher extension accuracy when dealing with shallow local anomalies and deep regional anomalies, and under the condition of shallow source model, the continuation error of the improved algorithm is only 60% of that of Landweber iterative method. At the same time, the measured data also verify that the improved method can achieve fast and high-precision downward continuation of a magnetic field.

Heuristic and random search algorithm in optimization of route planning for Robot’s geomagnetic navigation

To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.

Improved Particle Swarm Optimization Geomagnetic Matching Algorithm Based on Simulated Annealing

As a new assistant navigation technology using geophysical field for navigation, geomagnetic matching navigation can effectively alleviate the problems such as the unavailability of satellite and the easy divergence of position data of inertial navigation system in the process of navigation. It can also carry out real-time assistant navigation with high concealment, all-around area and all-weather. According to the principle of geomagnetic matching and the geomagnetic affine model, considering that the basic particle swarm optimization algorithm is easy to fall into local extremum, this paper introduces particle swarm optimization geomagnetic matching algorithm based on simulated annealing(SAPSO) for limitations of traditional matching algorithm. What’s more, the SAPSO is improved from three parts: constraints, parameters and function of fitness. Finally, the simulation analysis is carried out from five aspects to verify the effectiveness and accuracy of the improved SAPSO.

Super-Resolution Geomagnetic Reference Map Reconstruction Based on Dictionary Learning and Sparse Representation

Geomagnetic matching navigation plays a vital role in the navigation and guidance field, and the construction of geomagnetic reference maps plays a significant role in geomagnetic matching navigation. This paper addresses the problem of generating high-resolution geomagnetic reference maps with limited measured data. Most existing methods can hardly satisfy the accuracy requirements. We base our study on the theory of image super-resolution reconstruction and approach this problem from the perspective of dictionary learning and sparse representation. We propose a method of sparse dictionary initialization based on prior information from rectangular harmonic analysis, and then the K-SVD algorithm is applied to the dictionary training process to improve the performance of the sparse dictionary. Three components of the geomagnetic field are considered for multi-channel sparse representation to enhance the quality of the constructed maps. Experimental results show that our proposed method outperforms other geomagnetic reference map construction methods in the reconstructed precision as well as the robustness to noise.

Application of Particle Swarm Optimization Algorithm in Geomagnetic Matching Navigation

In order to improve the positioning accuracy of the traditional geomagnetic matching algorithm, a new geomagnetic matching algorithm based on particle swarm optimization is proposed. The algorithm plans the search range of the real position centering on the measurement position of the reference navigation system. Extracting the corresponding geomagnetic data from the reference geomagnetic map, the particle swarm optimization algorithm is then introduced into the matching region search, and the obtained geomagnetic profile values are taken as particles in the particle population. On this basis, the normalized product correlation function is used as the particle fitness function, comparing the similarity between the reference sub graph and the real-time graph profile by the maximum fitness measure, Finally, through simulation analysis, the matching precision and matching time of matching algorithm based on ICP and PSO are compared. The simulation results show that the matching effect based on PSO algorithm is better than the traditional ICP algorithm. Although the matching time is slightly longer than the ICP algorithm, it meets the real-time requirements. Geomagnetic correlation matching algorithm is an extremely important type of algorithm in geomagnetic aided navigation algorithm. The current research focuses on the improvement of the traditional Terrain Contour Matching (TERCOM) algorithm metric function and the introduction of new metric functions and various ICP geomagnetic matching algorithms [1-4].The geomagnetic correlation matching algorithm of the aircraft not only requires high precision and high reliability, but also the execution efficiency of the algorithm is one of the important contents of the research. Based on the research of traditional geomagnetic correlation matching algorithm, this paper introduces particle swarm optimization algorithm into geomagnetic correlation matching algorithm. From the aspects of similarity measure function, geomagnetic matching feature quantity and search strategy, a new method of geomagnetic correlation matching for aircraft is proposed.

Geomagnetic matching navigation algorithm based on robust estimation

The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.

The Vector Matching Method in Geomagnetic Aiding Navigation.

In this paper, a geomagnetic matching navigation method that utilizes the geomagnetic vector is developed, which can greatly improve the matching probability and positioning precision, even when the geomagnetic entropy information in the matching region is small or the geomagnetic contour line’s variety is obscure. The vector iterative closest contour point (VICCP) algorithm that is proposed here has better adaptability with the positioning error characteristics of the inertial navigation system (INS), where the rigid transformation in ordinary ICCP is replaced with affine transformation. In a subsequent step, a geomagnetic vector information fusion algorithm based on Bayesian statistical analysis is introduced into VICCP to improve matching performance further. Simulations based on the actual geomagnetic reference map have been performed for the validation of the proposed algorithm.

Simulation Research on GM Navigation Based on AFSA

Geomagnetic matching (GM) is a new developmental technology in recent years. For resolving the cumulate errors of position, velocity, attitude in an inertial navigation system (INS), utilizing artificial fish swarm algorithm (AFSA) to carry on GM is proposed, and then the attained matching position regarded as measurement of filter to achieve the emendation of INS. Firstly, affine transformation model between inertial and real trajectories is displayed and the principle of INS/GM is explained. Secondly, the state of artificial fish (AF), distance and food consistence are defined afresh, and then the flow chart based on AFSA is given. Lastly, simulation analysis is performed in actual geomagnetic reference map (GRM). The results show that the algorithm actualizes the GM when the inertial system exist position, velocity and attitude error, that is the algorithm got the optimized global solution, with that, the data fusing is finished with the results that not only reduce error evidently but also verify the validity and feasible of the algorithm.