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arXiv · 2603.29512

Communication Outage-Resistant UUV State Estimation: A Variational History Distillation Approach

Shuyue Li·Miguel López-Benítez·Eng Gee Lim·Fei Ma·Qian Dong·Mengze Cao·Limin Yu·Xiaohui Qin
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Abstract

The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent interruptions. When communication outages occur, standard state estimators such as the Unscented Kalman Filter (UKF) will be forced to make open-loop predictions. If the environment contains unmodeled dynamic factors, such as unknown ocean currents, this estimation error will grow rapidly, which may eventually lead to mission failure. To address this critical issue, this paper proposes a Variational History Distillation (VHD) approach. VHD regards trajectory prediction as an approximate Bayesian reasoning process, which links a standard motion model based on physics with a pattern extracted directly from the past trajectory of the UUV. This is achieved by synthesizing ``virtual measurements'' distilled from historical trajectories. Recognizing that the reliability of extrapolated historical trends degrades over extended prediction horizons, an adaptive confidence mechanism is introduced. This mechanism allows the filter to gradually reduce the trust of virtual measurements as the communication outage time is extended. Extensive Monte Carlo simulations in a high-fidelity environment demonstrate that the proposed method achieves a 91% reduction in prediction Root Mean Square Error (RMSE), reducing the error from approximately 170 m to 15 m during a 40-second communication outage. These results demonstrate that VHD can maintain robust state estimation performance even under complete communication loss.

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BibTeXRIS

Shuyue Li, Miguel López-Benítez, Eng Gee Lim, Fei Ma, Qian Dong, Mengze Cao, Limin Yu, Xiaohui Qin. 2026-03-31. Communication Outage-Resistant UUV State Estimation: A Variational History Distillation Approach. https://doi.org/10.1109/oceans66983.2026.11617142

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