Research on Adaptive Regional Classification and Prediction for Underwater Navigation Based on Genetic Algorithm and Support Vector Machine Model

Wenjie Zhang

doi:10.54097/966rcj27

Authors

Wenjie Zhang

DOI:

https://doi.org/10.54097/966rcj27

Keywords:

Gravity matching navigation, Support vector Machine, Suitable area.

Abstract

This paper primarily investigates the selection of matching regions in a gravity matching navigation system for submersibles. When employing Support Vector Machines (SVM) for adaptive region classification, traditional SVMs necessitate manual adjustment of hyperparameters according to different scenarios. To address this limitation, our work integrates Genetic Algorithm (GA) with SVM for the selection of suitable matching regions in underwater gravity-assisted navigation. The experimental results demonstrate that this approach can provide valuable references for the division of adaptive regions and the design of navigation routes for underwater vehicles.

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References

[1] Zou,J.S, Xiao,y, Sun,A.B, Wei,J.C, Meng,N, and Ai,S.J, “Research on relationship between gravity anomaly feature quality and matching navigation algorithm,” Science of Surveying and Mapping, vol. 45, no. 10, pp. 33–40, 2020, doi: 10.16251/j.cnki.1009-2307.2020.10.006.

[2] Chen,K.N, Xiao,Y, Zhang,B.J, Cao,J, Wang,Y.K, and Hong,X.D, “Comparison of Adaptive Evaluation Meth ods of Underwater Gravity Matching Navigation,” Journal of Geodesy and Geodynamics, pp. 1–12, doi: 10.14075/j.jgg.2023.10.176.

[3] Z. Wang, Y. Huang, M. Wang, J. Wu, and Y. Zhang, “A Computationally Efficient Outlier-Robust Cubature Kalman Filter for Underwater Gravity Matching Navigation,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–18, 2022, doi: 10.1109/TIM.2022.3141153.

[4] W. Ai, S. Zhong, Y. Leng, and S. Wang, “A Real-Time Autonomous Gravity Measurement Method for Underwater Gravity-Aided Navigation,” IEEE Access, vol. 11, pp. 135728–135737, 2023, doi: 10.1109/ACCESS.2023.3338537.

[5] Xiao,Y, Zhang,Y.J, Cao,J, Chen,K.N, Wang,Y.K, and Hong,X.D, “Suitability Analysis of Gravity Matching Navigation Based on Multiple Attribute Decision Making Theory,” Geomatics and Information Science of Wuhan University, vol. 48, no. 7, pp. 1089–1099, 2023, doi: 10.13203/j.whugis20230073.

[6] M. A. Ganaie and M. Tanveer, “KNN weighted reduced universum twin SVM for class imbalance learning,” Knowledge-Based Systems, vol. 245, p. 108578, Jun. 2022, doi: 10.1016/j.knosys.2022.108578.

[7] V. Ranaee, A. Ebrahimzadeh, and R. Ghaderi, “Application of the PSO–SVM model for recognition of control chart patterns,” ISA Transactions, vol. 49, no. 4, pp. 577–586, Oct. 2010, doi: 10.1016/j.isatra.2010.06.005.

[8] G. Acampora, A. Chiatto, and A. Vitiello, “Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm,” 2023.

[9] [9] B. Alhijawi, “Genetic algorithms: theory, genetic operators, solutions, and applications”.

[10] V. Vapnik, S. E. Golowich, and A. J. Smola, “Support Vector Method for Function Approximation, Regression Estimation and Signal Processing”.