Application of state estimation algorithm for UUV docking

CHEN Weixin; LIU Tao; ZHANG Tao; LIU Feng

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

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > Accepted Manuscript

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of state estimation algorithm for UUV docking[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0161

Citation:

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of state estimation algorithm for UUV docking[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0161

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of state estimation algorithm for UUV docking[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0161

Citation:

CHEN Weixin, LIU Tao, ZHANG Tao, LIU Feng. Application of state estimation algorithm for UUV docking[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0161

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Application of state estimation algorithm for UUV docking

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 achieving long-range cooperative of UUV. Aiming at the problem of insufficient estimation of the motion state of the docking device in docking, an interacting multi-model adaptive unscented Kalman filter method is proposed to estimate the docking device motion state. Considering that the measurement error of the motion state of the docking device obtained by the UUV sensor is large, the UUV nonlinear observation model is established, and the adaptive unscented Kalman filter (AUKF) algorithm is used to update the observation noise model in real time to reduce the observation error. Considering that the difficulty of the describing the relative motion of UUV and docking device with a single model, the motion state model set of the docking device is established, and the interactive multi-model algorithm is used to describe the motion state of the docking device to improve the filtering accuracy and realize the accurate estimation of the motion state of the docking device by UUV in underwater docking. Based on UUV docking test data, the estimation results of unscented Kalman filter, adaptive unscented Kalman filter and interacting multi-model adaptive unscented Kalman filter are compared. The results show that the accuracy and stability of interacting multi-model adaptive unscented Kalman filter are better than the other two algorithms. It can be applied to underwater autonomous docking scenarios with UUV to improve the success rate.
- unmanned undersea vehicle,
- underwater docking,
- unscented Kalman filter,
- noise covariance,
- interacting multiple model

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References(15)

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