Research on Underwater Navigation Adaptation Area Classification Prediction Based on Support Vector Machine Model

Abstract

 This paper focuses on the selection of adaptation areas for gravity matching navigation in marine regions, using gravity anomaly benchmark data as the foundation. A Support Vector Machine (SVM) model is established, and the model is verified and studied for its complexity. Initially, the gravity anomaly benchmark data is cleaned and preprocessed to ensure the accuracy and consistency of the information. Subsequently, based on the principal component analysis (PCA) criterion, four gravity field characteristic parameters of the adaptability designation for each area are screened, resulting in two feature attribute indicators for judging regional adaptability: average gravity change and gravity field slope standard deviation. Through feature extraction from the refined gravity anomaly benchmark map, the classification of adaptation areas is predicted using a Support Vector Machine under non-linear separable conditions. Finally, for new gravity anomaly benchmark data, a translatability prediction is conducted on the classification system, achieving a good fitting effect. This indicates that System has significant applicability to new gravity anomaly data. It performs excellently in processing new gravity anomaly data, effectively interpreting and simulating the characteristics and patterns of these data. The system can accurately capture non-linear relationships and complex dynamic changes within the data, demonstrating good adaptability and robustness.