The Data-Driven High-Precision Geomagnetic Matching-Assisted Navigation Algorithm Under Satellite Denial Conditions

In cases of satellite signal interference or even satellite denial, to improve the positioning accuracy of navigation systems, a Sparrow Search Algorithm (SSA) is used to optimize the weights and biases of the Extreme Learning Machine (ELM), and an AdaBoost ensemble learning mechanism is introduced. This results in the SSA-ELM-AdaBoost geomagnetic matching-assisted navigation algorithm. In an ideal 2D plane environment, the SSA-ELM-AdaBoost algorithm shows a clear advantage in matching accuracy compared to other algorithms (such as BP, ELM, XGBoost, SSA-XGBoost, SSA-ELM), achieving millimeter-level precision that meets high-accuracy positioning requirements. Despite the complexity of geomagnetic matching in 3D space, the algorithm maintains high accuracy, especially in the height direction, where the precision can reach centimeter-level with a maximum error of 10-20 meters. In simulations of complex real-world environments where noise is added to the prediction data, the SSA-ELM-AdaBoost algorithm demonstrates strong robustness. The average matching error in the latitude and longitude directions remains within 10 meters. Although occasional outliers of around 100 meters may occur in the latitude direction, their probability is very low, and they can be prevented from affecting the positioning system through filtering algorithms. The algorithm performs well in computational real-time, efficiently processing large amounts of sample data, meeting engineering application requirements, and demonstrating its potential in complex environments.