Geomagnetic Matching Algorithm Research Articles

An innovative PSO-ICCP matching algorithm for geomagnetic navigation

Geomagnetic aided navigation is an effective approach to eliminate the cumulative error in inertial navigation system (INS). Iterative closest contour point (ICCP) geomagnetic matching algorithm is sensitive to initial error, which will degrade the navigation accuracy greatly. To solve the problem, ICCP and particle swarm optimization (PSO) are improved separately, and an innovative PSO-ICCP geomagnetic matching algorithm is proposed. Firstly, a multi-attribute decision-making problem is introduced to optimize the output of ICCP. Secondly, PSO and ICCP are combined to diminish the sensitive of ICCP to initial error with the global search capability of PSO. Finally, to improve the performance of PSO, a particle initialization strategy is adopted using sliding window and quad-tree. Experimental results verified that PSO-ICCP can effectively reduce INS error. When magnetic measurement noise is 15nT, the position error and heading error are reduced from 1318.83 m, 7.83° to 23.51 m, 2.08°, which is more accurate compared with state-of-art algorithm.

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.

An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

To improve the positioning accuracy of an inertial/geomagnetic integrated navigation algorithm, a combined navigation method based on matching strategy and hierarchical filtering is proposed. First, the PDA-ICCP geomagnetic matching algorithm is improved. On basis of evaluating the distribution of magnetic measurements, a number of controllable magnetic values are regenerated to participate in the geomagnetic matching algorithm (GMA). As a result, accuracy of the matching algorithm is ensured and its efficiency is improved. Secondly, the integrated navigation filter is designed based on the hierarchical filtering strategy, in which the navigation information of the geomagnetic matching module and inertial navigation module are respectively filtered and fused in the main filter. In this way, the shortcoming that GMA is unable to provide continuous and real-time navigation information is overcome. Meanwhile, precision of the inertial/geomagnetic integrated navigation algorithm is improved. Finally, the feasibility and validity of the proposed algorithm are verified by simulation and physical experiments. Compared with the integrated filtering algorithm which directly uses the error equation of inertial navigation system (INS) as the state equation, the proposed hierarchical filtering algorithm can achieve higher positioning precision.

Performance Test of MPMD Matching Algorithm for Geomagnetic and RFID Combined Underground Positioning

It is important that precise positioning and navigation of underground personnel for intelligent hedging and mine rescue in mine accidents or disasters. At present, there are many passive positioning methods such as RFID, Zigbee and other positioning products in the underground, which cannot work normally while there is power supply interruption or signal interruption in the underground. GRPM is a new active underground positioning method combining radio frequency identification and geomagnetic matching technology, which the positioning accuracy reach to the sub-meter level when the miner with the GRPM device in the underground. Geomagnetic matching algorithm is the core technology of GRPM, and its performance directly affects the accuracy of the geomagnetic positioning. For the MSD, MAD and PORD matching algorithms have low precision in geomagnetic matching localization; it is proposed a novel matching algorithm named MPMD to achieve underground geomagnetic precise positioning in this paper which is an optimal estimation of the eigenvector product of underground two magnetic variables. The matching test of underground geomagnetic positioning for the performance of MPMD algorithm was carried out in 9 tunnels about 150 meters long and 6 meters wide in mine. The geomagnetic data of the total magnetic field and the three axis component are measured with the portable FVM400 fluxgate magnetometer. MSD, MAD and MPMD algorithms were used to matching test for analyzed their effective matching length, positioning error, noise immunity. The test result shows that the MPMD algorithm has the advantages of minimum matching position error and better noise immunity than MSD, MAD matching algorithms under the same experimental conditions. It is suitable for the complications of underground geomagnetic spatial distribution, and meets the requirements of underground emergency refuge and navigation. However, matching process of MPMD algorithm is time-consuming, it can be further improved the search space and search strategy in later research.

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.

An Improved Method of Geomagnetic Aided Inertial Navigation Algorithm with Gyro and Accelerometer Error Corrected Online

In consideration of the problem that traditional geomagnetic aided navigation method cannot reduce the scaling error of indication track in inertial navigation system (INS), which will further limit the error correction precision of gyro and accelerometer, an improved geomagnetic matching algorithm based on affine transformation is proposed in this paper. A geomagnetic matching algorithm led to the optimal affine transformation solution by Procrustes analysis is presented and develops latitude and longitude reference information. Then a 13-dimensional-state extended Kalman filter which estimates the attitude misalignment angles, the position error, the velocity error, the Gyro drift, and accelerometer error is introduced to continuously update the output of INS and remove the accumulative error. The results show that geomagnetic aided navigation based on improved algorithm has better location accuracy and correction accuracy of INS than the traditional method.

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.

Research of Geomagnetic Matching Algorithm Based on Artificial Fish Swarm Searching Strategy

Geomagnetic matching is a new developmental technology in recent years. For resolving the question of the searching space is too much, a geomagnetic matching algorithm based on AFSSS is proposed, which imitates the fish behaviors such as the preying, swarming, following and leaping etc to achieve the global optimum value, then achieve the emendation of inertial system. Firstly, the affine transformation model between inertial trajectory and real trajectory is displayed. Secondly, from the application of geomagnetic matching point of view, the state of artificial fish (AF), distance and food consistence are defined afresh, and the algorithm flow chart based on AFSSS is given. Lastly, the simulation analysis is performed in an actual magnetic reference map. The simulation results show that the algorithm actualizes the precision localization when the inertial system exist position error, velocity error and heading error and then validates the feasibility of the proposing method.

An Improved Geomagnetic Matching Algorithm Using Rotation Angle Search Technique

The accumulation course angle error of inertial navigation system will decrease the accuracy and reliability of an geomagnetism aided inertial navigation system using a geomagnetic contour matching algorithm. To improve the matching accuracy, the matching track and true track should be as parallel as possible. An improved geomagnetic matching algorithm is presented by introducing rotation angle search technique. To reduce the computation burden, improve operation efficiency and reduce false matching probability, a new search area determination method is proposed, which redefines the search region and reduces the search range. Simulation results demonstrate the effectiveness of the proposed algorithm and the improvement in the matching accuracy.

A New Correlation Matching Algorithm Based on Differential Evolution for Aircraft Geomagnetic Aid Navigation

Considering the requirements of the high accuracy, high reliability and real-time capability for the aircraft geomagnetic correlation matching algorithm, the similarity core function, geomagnetic properties and search methods will be processed synthetically in this paper. The correlation variance is introduced as the similarity core function, the feature normalization of the geomagnetic field is introduced as the matching parameters and the differential evolution algorithm is introduced for reducing the running time. The experiments have been done in the laboratory based on the actual measured geomagnetic field data, its results show that: the matching probability and accuracy of the new algorithm are better than the traditional one on the whole and the average running time is shortened from 7～11s to 1～2s.