Geomagnetic-Guided Underwater Navigation With Gradient Multiattention Neural Networks

Due to the absence of global navigation satellite systems (GNSSs) in the underwater environment, navigation utilizing the geophysical magnetic fields of the Earth (GMFE) emerges as a promising field. Geomagnetic gradient-based navigation systems rely on premapping designated areas to minimize the effects of magnetic anomalies on navigation performance. However, obtaining magnetic maps beforehand is challenging, and relying solely on historical maps is insufficient for describing magnetic anomalies. Significant changes in magnetic field gradients caused by unidentified anomalies can render conventional geomagnetic navigation methods ineffective. In this article, we present a novel method called the geomagnetic gradient multiattention neural network (GGMA). Our proposed encoder and decoder attention mechanism is capable of predicting the position and geomagnetic trends based on various geomagnetic gradient elements extracted from the trajectory data. We additionally utilize maximum likelihood estimation (MLE) to ascertain the probability of the current coordinates being located within the anomalous region. By integrating the expected heading with the calculated heading, the effect of magnetic field anomalies on geomagnetic navigation is minimized. The GGMA method is effective in solving the navigation failure problem of magnetic field gradient-based navigation methods in the presence of magnetic field anomalies during long-distance simulation navigation experiments. It has higher navigation efficiency and accuracy compared with other algorithms in regions without anomalies.