A Bionic Data-Driven Approach for Long-Distance Underwater Navigation With Anomaly Resistance

Various animals exhibit accurate navigation using environmental cues. The Earth’s magnetic field has been proven to be a reliable information source for long-distance fauna migration. Inspired by the animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for 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 navigation. To mitigate the impact of geomagnetic anomalies, we develop a mechanism to detect and quantify the anomalies based on maximum likelihood estimation (MLE). 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 the resilience of our navigation approach against geomagnetic anomalies, along with the precision and stability of underwater navigation in single and multiple destination missions.