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A multi-AUV cooperative navigation method based on the augmented adaptive embedded cubature Kalman filter algorithm

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Abstract

Cooperative navigation is part of the essential method for multiple autonomous underwater vehicles (AUVs) to gain an accurate position when performing tasks underwater. However, the positioning accuracy and stability of the traditional algorithms are affected by the dimension of state-space and the unknown time-varying noise in the ocean, which cannot meet the demand of the increasing positioning performance. For this problem, we propose a cooperative navigation method based on the augmented adaptive embedded cubature Kalman filter (A-AECKF) algorithm. Due to the non-additivity of the system noise in the realistic model of multi-AUV cooperative navigation, we augment the dimension of the state variables firstly, which combines with the system model to estimate the noise. Then we adopt embedded cubature criterion reselecting cubature points and their weights to minimize the positioning error caused by state augmentation. Finally, a nonlinear noise statistical estimator is used to estimate the time-varying ranging noise in real time, which effectively suppresses the positioning error caused by the non-Gaussian white noise. We evaluate the performance of the proposed A-AECKF cooperated navigation method comprehensively under the circumstance with high-dimensional state-space and the unknown time-varying measurement noise. Compared with other related algorithms, the experimental results indicate that the presented method possesses higher positioning accuracy and stability.

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Abbreviations

A-AECKF:

Augmented adaptive embedded cubature Kalman filter

ECKF:

Embedded Cubature Kalman filter

CKF:

Cubature Kalman filter

AUV:

Autonomous underwater vehicle

EKF:

Extended Kalman filter

UKF:

Unscented Kalman filter

KF:

Kalman filter

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Acknowledgements

This work is partly supported by the Major Scientific and technological innovation project of Shandong Province of China (2020CXGC010705, 2021ZLGX05), China Postdoctoral Science Foundation funded project (2020M672123), Post-doc Creative Funding in Shandong Province (244312), and Post-doc Funding in Weihai, science and technology development plan project of Weihai.

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Authors and Affiliations

  1. School of Information Science and Engineering, Harbin Institute of Technology, Weihai, No. 2 Wenhua West Road, Weihai, Shandong Province, 264209, China

    Qinghua Luo, Yang Shao, Jianfeng Li, Xiaozhen Yan & Chao Liu

  2. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, China

    Qinghua Luo, Yang Shao, Jianfeng Li, Xiaozhen Yan & Chao Liu

  3. The Auto Test and Control Institute, Shandong Institute of Shipping, No. 2 Wenhua West Road, Weihai, Shandong Province, 264209, China

    Qinghua Luo, Yang Shao, Jianfeng Li, Xiaozhen Yan & Chao Liu

  4. Shandong New Beiyang Information Technology Co., Ltd, Weihai, Shandong Province, 264203, China

    Qinghua Luo

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  1. Qinghua Luo
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  2. Yang Shao
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Correspondence to Qinghua Luo.

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Luo, Q., Shao, Y., Li, J. et al. A multi-AUV cooperative navigation method based on the augmented adaptive embedded cubature Kalman filter algorithm. Neural Comput & Applic 34, 18975–18992 (2022). https://doi.org/10.1007/s00521-022-07450-8

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  • DOI: https://doi.org/10.1007/s00521-022-07450-8

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