Academic Journal of Computing & Information Science, 2024, 7(7); doi: 10.25236/AJCIS.2024.070708.
Wang Jiawen
Department of Mechanical and Electronic Engineering, Shenyang Aerospace University, Shenyang, China
In this paper, the classification and prediction technology of underwater navigation adaptation area based on gravity anomaly data is studied. Firstly, the gravity anomaly reference data is processed by interpolation encryption, and the region division and adaptation calibration are completed by cluster analysis. Secondly, latitude and longitude are selected as the attribute index, and the adaptive area classification prediction model based on CRNN neural network is established. Finally, another set of gravity anomaly data is used to predict the migration of the model, and the validity of the model is verified. The research shows that the adaptive region classification and prediction technology based on gravity anomaly data can significantly improve the accuracy of underwater navigation. The research is of great significance for establishing an accurate and efficient underwater navigation system.
Underwater navigation, Gravity anomaly, Adaptation area, Cluster analysis, CRNN neural network
Wang Jiawen. Research on classification and prediction technology of underwater navigation adaptation area based on gravity anomaly data. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 7: 60-64. https://doi.org/10.25236/AJCIS.2024.070708.
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