Data-driven classification prediction of underwater navigation adaptation area

Rui Song; Tingting Yan; Xuhao Zhou; Liting Han

doi:10.1117/12.3036590

21 July 2024 Data-driven classification prediction of underwater navigation adaptation area

Rui Song, Tingting Yan, Xuhao Zhou, Liting Han

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 1321947 (2024) https://doi.org/10.1117/12.3036590
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

This paper investigates the problem of classifying and predicting fitness zones for underwater gravity-aided navigation. Firstly, a refined baseline map is generated by interpolating and filling in missing data. Based on the methods of repetitive simulation and machine learning, we compare the machine learning KNN, SVM, PSO-BP neural network with the integrated learning XGBoost, RF, MARS and other models, and finally determine to establish the classification prediction system of SVM. Secondly, the idea of area micronutrient is applied to transform the points into rectangular area micronutrients. Through K-means clustering calculation, the calibration of the fitness area is completed, including excellent, good, average, and poor fitness area. A starting point is established based on the area microelement, and this point is geometrically processed with its 8 neighboring orientation points to produce 9 new regional characteristic attribute indicators. The most representative and critical 12 regional characteristic attribute indicators are finally determined. The AUCs of SVM, RF, and XGBoost with hyperparameter optimization of the data were 0.97, 0.90, and 0.91, and then, considering the simplicity and robustness of the processing problem, the underwater fitness zone classification prediction system based on SVM was finally established, and the prediction of the model migratory was carried out.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Song, Tingting Yan, Xuhao Zhou, and Liting Han "Data-driven classification prediction of underwater navigation adaptation area", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 1321947 (21 July 2024); https://doi.org/10.1117/12.3036590

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KEYWORDS

Interpolation

Data modeling

Calibration

Machine learning

Data processing

Classification systems

Reflection

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