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An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles

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Abstract

In order to combat the severity of the impact of short-term failure Doppler velocity log (DVL), we propose a machine learning (ML) aided method for strapdown inertial navigation system (SINS)/DVL integration solution. First, the inherent relationship between the underwater vehicle’s dynamics characteristic and the SINS’s velocity error is established through the learning methodology of the least square support vector machine (LS-SVM), and the prediction and compensation are performed during the failure period of the DVL. When the DVL signal is normal, the LS-SVM model is trained, the adaptive Kalman filtering (AKF) is adopted in the SINS/DVL integrated navigation system, the filtering estimation value is used to correct the SINS’s navigation calculation value. When the DVL signal is invalid, the variation of underwater vehicle movement is taken as the input of the LS-SVM model. Land vehicle field experiment is conducted to verify the feasibility and effectiveness of the LS-SVM/AKF algorithm aided SINS/DVL integrated navigation system. The results indicate that the proposed methodology can improve the accuracy of the SINS/DVL integrated navigation system during short-term failure of DVL.

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Acknowledgements

This work partially supported by the Natural Science Research Project of Jiangsu Higher Education Institutions under Grant 19KJB510052, the National Science Foundation of Jiangsu Province under Grant BK20200763, the China Postdoctoral Science Foundation under Grant 2020M681685, the Postdoctoral Research Funding Project of Jiangsu Province under Grant 2021K161B, the NUPTSF under Grant NY219023, Open Fund of State Key Laboratory of Ocean Engineering under Grant GKZD010084.

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Authors and Affiliations

  1. College of Internet of Things, Nanjing University of Posts and Telecommunication, Nanjing, 210003, China

    Jin Sun

  2. Jiangsu Huaerwei Science and Technology Group Co., Ltd, Huaian, 211600, China

    Jin Sun

  3. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jin Sun

  4. East China Institute of Photo-Electron IC, Suzhou, 215129, China

    Fu Wang

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  1. Jin Sun
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  2. Fu Wang
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Correspondence to Jin Sun.

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Sun, J., Wang, F. An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles. Peer-to-Peer Netw. Appl. 15, 1437–1451 (2022). https://doi.org/10.1007/s12083-022-01310-x

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