A Map-free Geomagnetic Navigation Method Based on the World Geomagnetic Model Position Estimation with a Fully Connected Neural Network

In map-free geomagnetic navigation conditions, the traditional matching algorithms will be ineffective, and the regular position searching optimization algorithms still face the problems of low navigation accuracy and inefficiency. How to further improve the accuracy and efficiency of the algorithm has become the key to the application of this method in maple’s geomagnetic navigation conditions. Based on the above background, this paper proposes an evolutionary gradient search navigation algorithm optimized via position estimation (PE-EGA). The world geomagnetic model (WMM) is used to establish the nonlinear correlation relationship between geographic position and geomagnetic features, and the inverse mapping of the geomagnetic model is fitted by a fully connected neural network to get the rough estimation of the geographic position of the vehicle, with a root mean square error (RMSE) of 0.0121 in position estimation. Finally, the information of the rough estimation is used to assist the decision-making of the navigational azimuth angle involved in the EGA algorithm. The simulation results show that the offset distance of the improved algorithm is only 27.09 m, and the path ratio reaches 1.0178 with an error ratio of 0.38%. Comparative study using measured geomagnetic data of Boao town with model data shows that the final offset distance is only 51.63 m, path ratio 1.0036, and error ratio 0.73%, which significantly improves the accuracy and timeliness of navigation compared to the original EGA algorithm. This article provides an innovative and practical solution strategy for map-free geomagnetic navigation.