  Various animals exhibit accurate navigation using environment cues. The
Earth's magnetic field has been proved a reliable information source in
long-distance fauna migration. Inspired by animal navigation, this work
proposes a bionic and data-driven approach for long-distance underwater
navigation. The proposed approach uses measured geomagnetic data for the
navigation, and requires no GPS systems or geographical maps. Particularly, we
construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM)
network to predict the heading angle during the navigation. To mitigate the
impact of geomagnetic anomalies, we develop the mechanism to detect and
quantify the anomalies based on Maximum Likelihood Estimation. We integrate the
developed mechanism with the TA-LSTM, and calibrate the predicted heading
angles to gain resistance against geomagnetic anomalies. Using the retrieved
data from the WMM model, we conduct numerical simulations with diversified
navigation conditions to test our approach. The simulation results demonstrate
a resilience navigation against geomagnetic anomalies by our approach, along
with precision and stability of the underwater navigation in single and
multiple destination missions.
