A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation

XIAO Ruibin; MA Tiefeng; HU Youfeng

doi:10.11993/j.issn.2096-3920.2025-0156

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

XIAO Ruibin, MA Tiefeng, HU Youfeng. A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0156

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XIAO Ruibin, MA Tiefeng, HU Youfeng. A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0156

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A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation

doi: 10.11993/j.issn.2096-3920.2025-0156

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

Received Date: 2025-11-12
Accepted Date: 2025-12-25
Rev Recd Date: 2025-12-16

Available Online: 2026-01-12

Abstract

Abstract

When facing progressive faults in the Doppler velocity log (DVL), traditional adaptive filters fail to provide effective fault tolerance in autonomous underwater vehicle(AUV) integrated navigation systems due to conflicts between noise estimation and fault detection. To address this issue, this paper proposes a collaborative fault-tolerant navigation method that integrates long short-term memory (LSTM) networks for fault detection with variational bayesian adaptive Kalman filter (VBAKF) and IGG-III robust filtering. The proposed approach utilizes LSTM networks to effectively identify early characteristics of progressive faults. Upon fault confirmation, the filter switches from VBAKF to IGG-III robust filtering mode, dynamically adjusting weights to mitigate fault impact. Normal operation resumes using VBAKF after fault resolution. Experimental results demonstrate that, in the event of DVL progressive faults, the proposed method achieves higher navigation accuracy than several mainstream filtering algorithms, effectively suppresses state estimation distortion, and enhances the precision and robustness of AUV integrated navigation systems in uncertain underwater environments.

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References(19)

References

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