Application of State Estimation Algorithm for Autonomous Underwater Docking of UUVs

CHEN Weixin; LIU Tao; ZHANG Tao; LIU Feng

doi:10.11993/j.issn.2096-3920.2024-0161

Volume 33 Issue 4

Aug 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(4): 713-721

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of State Estimation Algorithm for Autonomous Underwater Docking of UUVs[J]. Journal of Unmanned Undersea Systems, 2025, 33(4): 713-721. doi: 10.11993/j.issn.2096-3920.2024-0161

Citation:

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of State Estimation Algorithm for Autonomous Underwater Docking of UUVs[J]. Journal of Unmanned Undersea Systems, 2025, 33(4): 713-721. doi: 10.11993/j.issn.2096-3920.2024-0161

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Application of State Estimation Algorithm for Autonomous Underwater Docking of UUVs

doi: 10.11993/j.issn.2096-3920.2024-0161

The 705 Research Institute, China State Shipbuilding Corporation Limited, Kunming 650106, China

Received Date: 2024-11-22
Accepted Date: 2025-03-04
Rev Recd Date: 2025-02-26

Available Online: 2025-06-26

Abstract

Abstract

Underwater autonomous dynamic docking of unmanned undersea vehicle(UUV) is one of the key technologies for their long-range cooperative operations. In view of the insufficient estimation accuracy of the motion state of the docking device during underwater dynamic docking of UUVs, an interacting multiple model adaptive unscented Kalman filter(IMM-AUKF) algorithm was proposed to estimate the motion state. By considering the large measurement error of the motion state of the docking device obtained by the UUVs’ own sensor, the UUV nonlinear observation model was established, and the adaptive unscented Kalman filter(AUKF) algorithm was used to update the observation noise in real time and reduce observation errors. To accurately describe the relative motion between UUVs and the docking device with a single motion model, the relative motion model set of UUVs and the docking device was established, and the IMM was used to describe the motion state and improve the filtering accuracy. Based on UUV docking test data, the estimation results of the motion state of the docking device by the UKF, AUKF, and IMM-AUKF algorithms were compared. The results show that the accuracy and stability of the IMM-AUKF algorithm are better than the other two algorithms. It can meet the requirements of underwater dynamic docking and improve the success rate of UUV docking.
- unmanned undersea vehicle,
- underwater docking,
- state estimation,
- unscented Kalman filter,
- interacting multiple model

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References(15)

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