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

XIAO Ruibin; MA Tiefeng; HU Youfeng

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

Volume 34 Issue 1

Feb 2026

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > 34(1): 157-166

XIAO Ruibin, MA Tiefeng, HU Youfeng. A Fault-Tolerant Navigation Algorithm for AUVs Based on Collaborative Fault Detection and Robust Estimation[J]. Journal of Unmanned Undersea Systems, 2026, 34(1): 157-166. doi: 10.11993/j.issn.2096-3920.2025-0156

Citation:

XIAO Ruibin, MA Tiefeng, HU Youfeng. A Fault-Tolerant Navigation Algorithm for AUVs Based on Collaborative Fault Detection and Robust Estimation[J]. Journal of Unmanned Undersea Systems, 2026, 34(1): 157-166. doi: 10.11993/j.issn.2096-3920.2025-0156

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A Fault-Tolerant Navigation Algorithm for AUVs 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

In the integrated navigation system of autonomous undersea vehicles(AUVs), traditional adaptive filtering algorithms struggle to achieve effective fault tolerance against slowly varying faults of the Doppler velocity log(DVL) due to the conflict between noise estimation and fault detection mechanisms. To address this issue, this paper proposed a collaborative fault-tolerant navigation method fusing a long short-term memory(LSTM) network-based fault detection with a variational Bayesian adaptive Kalman filter(VBAKF) and IGG-III robust filtering. The proposed method utilized the LSTM network to effectively identify the early characteristics of slowly varying faults. Upon fault confirmation, the filter switched from the VBAKF to the IGG-III robust filtering mode and dynamically constructed equivalent weight matrices to suppress the influence of faulty measurements. After the fault ended, the VBAKF was restored to maintain optimal estimation. Experimental results demonstrate that in the event of DVL slowly varying faults, the proposed method achieves higher navigation accuracy than several mainstream filtering algorithms, effectively suppresses state estimation distortion, and enhances the positioning precision and robustness of the integrated navigation system of AUVs in uncertain underwater environments.
- autonomous underwater sea,
- fault-tolerant navigation algorithm,
- integrated navigation,
- long short-term memory,
- Kalman filter,
- robust estimation

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References(19)

References

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