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\begin{document}
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\title{Bionic Geomagnetic Navigation for Autonomous Underwater Vehicle with
Temporal Attention-based Data-Driven Dead Reckoning}



\author[1]{Songnan Yang }%
\author[2]{Xiaohui Zhang }%
\author[2]{Ding Liu}%
\author[2]{Wenqi Bai }%
\author[2]{Fan Liu}%
\affil[1]{Xian University of Technology}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 07, 2026}


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\begin{abstract}
\textbf{Numerous studies have demonstrated that numerous animal species
are capable of goal-directed navigation using environmental information
for dead reckoning. The stable magnetic field of the earth provides
important information for the migration of animals over long distances.
Inspired by the goal-directed navigation of animals, a novel Geomagnetic
Navigation with Temporal Attention-based Data-Driven Dead Reckoning
(Attention-DR) method for Autonomous Underwater Vehicles (AUV) is
presented in this article, which only utilizes the inclination angle
(\emph{I}) and the declination angle (\emph{D}) of the Geomagnetic Field
(GF) for underwater navigation without any prior knowledge of the
geographical location or geographic map. This article proposes a
Temporal Attention-based Long Short-Term Memory (TA-LSTM) neural network
by combining history, GF information, and location in time series to
achieve the optimization heading angle of underwater dead reckoning. Due
to its ability to adjust the utilization weights of local and global
temporal information in the path, TA-LSTM possesses the capability to
continually update neural network model parameters through a data-driven
process during the navigation process. This enables the Attention-DR to
achieve efficient navigation in regions without prior magnetic maps and
overcome navigation failures caused by magnetic field anomalies. The
simulation outcomes confirm the efficiency, precision, and
practicability of the proposed algorithm.}%
\end{abstract}%



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